-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  F:\Dropbox\Research\PSRM\replication file\data\appendix.log
  log type:  text
 opened on:  10 Dec 2025, 16:38:28

. 
. 
. 
. 
. 
.  *************************************************************
. **** Table A-2
. **************************************************************
. ** CFPS, imputated
. 
.  cd "$path\data\MI"
F:\Dropbox\Research\PSRM\replication file\data\MI

. 
. ***install misest package
. /*
> 
> 1) The package is located in the "mi" folder within the "code" folder.
> 2)read the "readme" file in the folder, which provides details for installing the package
> 3)Copy miest.ado, miest.hlp, misum.ado, and misum.hlp to your personal ado directory. 
> 4)use the "adopath" to check the ado directory, the fifth directory is your personal ado directory  */
. 
. 
.  
. clear

. 
. miest CFPSS logit censored rural age ccp school_yr hukou logfincome  male,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: censored

Number of Observations: 19752
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
   rural |   .61232     .0457236     13.392       3065    0.000
     age |  -.01828     .0015201    -12.026        254    0.000
     ccp |  -.85952     .1246315     -6.897      19798    0.000
school_yr | -.00346     .0061111     -0.566         24    0.577
   hukou |  -.50432      .097325     -5.182        505    0.000
logfincome |  .18031    .0207857      8.675        161    0.000
    male |   .35937      .041865      8.584      10261    0.000
   _cons |  -2.8557       .26077    -10.951        206    0.000
---------------------------------------------------------------


. 
. 
. ***CFPS, raw
. 
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
.  
.  use "CFPS_2014.dta",clear

. 
. 
. logit censored rural age ccp school_yr hukou logfincome male

Iteration 0:  Log likelihood =  -5315.401  
Iteration 1:  Log likelihood = -5067.8854  
Iteration 2:  Log likelihood = -5052.3642  
Iteration 3:  Log likelihood = -5052.3001  
Iteration 4:  Log likelihood = -5052.3001  

Logistic regression                                     Number of obs = 16,127
                                                        LR chi2(7)    = 526.20
                                                        Prob > chi2   = 0.0000
Log likelihood = -5052.3001                             Pseudo R2     = 0.0495

------------------------------------------------------------------------------
    censored | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       rural |   .7217991   .0610094    11.83   0.000     .6022229    .8413753
         age |  -.0245992   .0018876   -13.03   0.000    -.0282989   -.0208994
         ccp |  -.7194725   .1593858    -4.51   0.000    -1.031863    -.407082
   school_yr |  -.0340146   .0069656    -4.88   0.000     -.047667   -.0203623
       hukou |  -.3811557   .1323638    -2.88   0.004     -.640584   -.1217274
  logfincome |   .1135396   .0245642     4.62   0.000     .0653947    .1616845
        male |    .381118    .054384     7.01   0.000     .2745274    .4877086
       _cons |   -2.32486   .3155812    -7.37   0.000    -2.943388   -1.706332
------------------------------------------------------------------------------

. outreg2 using tableA2.doc,  bdec(3) sdec(3) nocons replace
tableA2.doc
dir : seeout

. 
. 
.  
.  
.  
. *************************************************************
. **** Table A-3
. **************************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. use "CHFS_2015.dta",clear

. 
. 
. 
.  **percentage
.   tab trustN0 if treat==0  /*percentage distribution for control group **/ 

     trustN |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        563        8.25        8.25
          1 |      1,510       22.12       30.36
          2 |      1,415       20.73       51.09
          3 |      1,209       17.71       68.80
          4 |      2,042       29.91       98.71
nonresponse |         88        1.29      100.00
------------+-----------------------------------
      Total |      6,827      100.00

.   
.   tab trustN0 if treat==1 /*percentage distribution for treatment group **/ 

     trustN |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        625        9.33        9.33
          1 |      1,262       18.83       28.16
          2 |      1,310       19.55       47.70
          3 |      1,289       19.23       66.94
          4 |      1,031       15.38       82.32
          5 |      1,107       16.52       98.84
nonresponse |         78        1.16      100.00
------------+-----------------------------------
      Total |      6,702      100.00

.   
.   
.  **mean
.  sum trustN if treat==0  /*mean value for control group **/ 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      trustN |      6,739    2.394272    1.339594          0          4

.  
.  sum trustN if treat==1 /*mean value for treatment group **/ 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      trustN |      6,624    2.628019    1.579607          0          5

.   
.   
.   
.   
.  
.  
. *************************************************************
. **** Table A-4
. **************************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. use "CHFS_2015_full.dta",clear

. 
.  **percentage
.   tab trustN0 if treat==0  // percentage distribution for control group 

     string |
   variable |
identifying |
nonresponse |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,369        8.80        8.80
          1 |      3,543       22.77       31.57
          2 |      3,195       20.54       52.11
          3 |      2,642       16.98       69.09
          4 |      4,567       29.35       98.44
nonresponse |        242        1.56      100.00
------------+-----------------------------------
      Total |     15,558      100.00

.   
.   tab trustN0 if treat==1 // percentage distribution for treatment group  

     string |
   variable |
identifying |
nonresponse |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,434        9.39        9.39
          1 |      3,033       19.86       29.25
          2 |      2,988       19.56       48.81
          3 |      2,832       18.54       67.35
          4 |      2,294       15.02       82.37
          5 |      2,492       16.32       98.69
nonresponse |        200        1.31      100.00
------------+-----------------------------------
      Total |     15,273      100.00

.   
.   
.  **mean
.  sum trustN if treat==0 // mean value for control group 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      trustN |     15,316    2.358775    1.351111          0          4

.  sum trustN if treat==1 // mean value for treatment group 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      trustN |     15,073    2.596762    1.584432          0          5

.   
.   
.   
.  
. **********************************************************
. ****Figure A-1 
. **********************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. use "CFPS_2014_full.dta",clear

. 
. 
. 
.  graph bar, over(trust_cadre)    ///
>       ytitle("Percent %",height(5) size(small))    bar(1, fcolor(white) color(black))  ///  
>            graphregion(color(white)) ylab(,nogrid) ///
>                      title("Full Sample",size(small) color(black)) ///
>                           blabel(bar, size(vsmall) format(%4.2f)) ///
>                           b1title("Level of Trust",height(5) size(small)) 

.         
. graph save "f1",replace
(file f1.gph not found)
file f1.gph saved

. 
.  graph bar if overlap==1, over(trust_cadre)    ///
>       ytitle("Percent %",height(5) size(small) )   bar(1, fcolor(white) color(black) )  ///  
>            graphregion(color(white)) ylab(,nogrid) ///
>                      title("74 Overlapping Cities",size(small) color(black)) ///
>                           blabel(bar, size(vsmall) format(%4.2f)) ///
>                            b1title("Level of Trust",height(5) size(small)) 

.                           
. graph save "f2",replace 
(file f2.gph not found)
file f2.gph saved

. 
. 
. graph combine f1.gph f2.gph,altshrink scheme(s1mono)    graphregion(fcolor(white)) row(1)                       

. 
. graph export "$path\fig_a1.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a1.pdf saved as PDF format

. 
. erase f1.gph

. erase f2.gph

. 
. 
.  
.  
. **********************************************************
. ****Figure A-2 
. **********************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. use "CFPS_2016_full.dta",clear

. 
. 
. 
.  graph bar, over(trust_cadre)    ///
>       ytitle("Percent %",height(5) size(small))    bar(1, fcolor(white) color(black))  ///  
>            graphregion(color(white)) ylab(,nogrid) ///
>                      title("Full Sample",size(small) color(black)) ///
>                           blabel(bar, size(vsmall) format(%4.2f)) ///
>                           b1title("Level of Trust",height(5) size(small)) 

.         
. graph save "f1",replace
(file f1.gph not found)
file f1.gph saved

. 
. 
. 
.  graph bar if overlap==1, over(trust_cadre)    ///
>       ytitle("Percent %",height(5) size(small) )   bar(1, fcolor(white) color(black) )  ///  
>            graphregion(color(white)) ylab(,nogrid) ///
>                      title("74 Overlapping Cities",size(small) color(black)) ///
>                           blabel(bar, size(vsmall) format(%4.2f)) ///
>                            b1title("Level of Trust",height(5) size(small)) 

.                           
. graph save "f2",replace 
(file f2.gph not found)
file f2.gph saved

. 
. 
. graph combine f1.gph f2.gph,altshrink scheme(s1mono)    graphregion(fcolor(white)) row(1)                       

. 
. graph export "$path\fig_a2.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a2.pdf saved as PDF format

. 
. erase f1.gph

. erase f2.gph

. 
. 
. 
. 
. 
. 
. **********************************************************
. ****Figure A-3
. **********************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. **panel A 
. clear

. 
. import excel "mean.xlsx", sheet("rural") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "Rural" 7 "Urban"  , angle(0) noticks labsize(small)) ///
> ylabel(0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%", angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel A: Rural/Urban",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2a", replace
(file f2a.gph not found)
file f2a.gph saved

. 
. 
. 
. **panel B
. clear

. 
. import excel "mean.xlsx", sheet("age") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "18-45" 7 "46+"  , angle(0) noticks labsize(small)) ///
> ylabel( 0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%", angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel B: Age",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2b", replace
(file f2b.gph not found)
file f2b.gph saved

. 
. 
. **panel C
. clear

. 
. import excel "mean.xlsx", sheet("ccp") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "CCP" 7 "Non-CCP"  , angle(0) noticks labsize(small)) ///
> ylabel(0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%", angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel C: CCP Membership",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2c", replace
(file f2c.gph not found)
file f2c.gph saved

. 
. 
. 
. 
. **panel D
. clear

. 
. import excel "mean.xlsx", sheet("college") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "College+" 7 "No College"  , angle(0) noticks labsize(small)) ///
> ylabel(0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel D: Education",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2d", replace
(file f2d.gph not found)
file f2d.gph saved

. 
. 
. 
. 
. 
. 
. **panel E
. clear

. 
. import excel "mean.xlsx", sheet("hukou") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "Local Hukou" 7 "Non-local Hukou"  , angle(0) noticks labsize(small)) ///
> ylabel(0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel E: Hukou",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2e", replace
(file f2e.gph not found)
file f2e.gph saved

. 
. 
. 
. **panel F
. clear

. 
. import excel "mean.xlsx", sheet("income") firstrow
(6 vars, 9 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str11
text was forced to string; some loss of information

. gen text1=substr(text, 1, 5)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> (scatter proportion row if group ==3, mcolor(black) msymbol(s) lcolor(black)  lpattern(solid) mlabel(txt_percent ) mlabposition(6)) /// 
> , legend(row(1) order(2 "CHFS" 3 "CFPS(MI)" 4 "CFPS" ) pos(6)) /// 
> xlabel(3 "Top 20%" 7 "Bottom 20%"  , angle(0) noticks labsize(small)) ///
> ylabel(0 "0%" 0.1 "10%" 0.2 "20%" 0.3 "30%" 0.40 "40%" 0.5 "50%", angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> ytitle("Estimated Proportion") title("Panel F: Household Income",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "f2f", replace
(file f2f.gph not found)
file f2f.gph saved

. 
. 
. graph combine f2a.gph f2b.gph f2c.gph f2d.gph f2e.gph f2f.gph ,altshrink scheme(s1mono) graphregion(fcolor(white)) row(2)                       
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph export "$path\fig_a3.pdf", as(pdf) name("Graph") replace
file F:\Dropbox\Research\PSRM\replication file\fig_a3.pdf saved as PDF format

. 
. erase f2a.gph

. erase f2b.gph

. erase f2c.gph

. erase f2d.gph

. erase f2e.gph

. erase f2f.gph

. 
. 
. 
. 
. 
. 
. 
. 
. 
.  
. **********************************************************
. ****Figure A-4 
. **********************************************************
. 
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

. 
. **panel A 
. clear

. 
. import excel "group_difference.xlsx", sheet("rural") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "Rural" 5.5 "Urban"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel A: Rural/Urban",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "fa", replace
(file fa.gph not found)
file fa.gph saved

. 
. 
. 
. **panel B
. clear

. 
. import excel "group_difference.xlsx", sheet("age") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "18-45" 5.5 "46+"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel B: Age",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "fb", replace
(file fb.gph not found)
file fb.gph saved

. 
. 
. **panel C
. clear

. 
. import excel "group_difference.xlsx", sheet("ccp") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "CCP" 5.5 "Non-CCP"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel C: CCP Membership",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "fc", replace
(file fc.gph not found)
file fc.gph saved

. 
. 
. 
. 
. **panel D
. clear

. 
. import excel "group_difference.xlsx", sheet("college") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "College+" 5.5 "No College"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel D: Education",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. 
. graph save "fd", replace
(file fd.gph not found)
file fd.gph saved

. 
. 
. 
. 
. 
. 
. **panel E
. clear

. 
. import excel "group_difference.xlsx", sheet("hukou") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "Local Hukou" 5.5 "Non-local Hukou"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel E: Hukou",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph save "fe", replace
(file fe.gph not found)
file fe.gph saved

. 
. 
. 
. **panel F
. clear

. 
. import excel "group_difference.xlsx", sheet("income") firstrow
(6 vars, 7 obs)

. 
. set scheme s1mono

. 
. gen percent=100*round(proportion,.0001)
(3 missing values generated)

. tostring percent, gen(text) force
text generated as str12
text was forced to string; some loss of information

. gen text1=substr(text, 1, 6)

.  
. gen p="%"

. egen txt_percent = concat(text1 p) 

. 
. twoway ///
> (rcap low95 high95 row, vert ) /// 
> (scatter proportion row if group ==1, mcolor(gs8)  msymbol(o) color(gs8) lpattern(solid) mlabel(txt_percent ) mlabposition(10)) /// 
> (scatter proportion row if group ==2, mcolor(gs8) msymbol(d)  lcolor(gs8)  lpattern(dash) mlabel(txt_percent ) mlabposition(8)) /// 
> , legend(row(1) order(2 "CHFS - CFPS" 3 "CHFS - CFPS(MI)" ) pos(6)) /// 
> xlabel(2.5 "Top 20%" 5.5 "Bottom 20%"  , angle(0) noticks labsize(small)) ///
> ylabel(-0.4 "-40%" -0.3 "-30%" -0.2 "-20%" -0.1 "-10%" 0 "0%" 0.1 "10%" , angle(0)) /// 
> xtitle("Local Political Trust",height(5)) ///
> yline(0, lpattern(dash) lcolor(gs8))  xtitle("") ///
> ytitle("Mean Difference of Local Political Trust") title("Panel F: Household Income",height(5) size(normarlsize))
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. 
. graph save "ff", replace
(file ff.gph not found)
file ff.gph saved

. 
. 
. graph combine fa.gph fb.gph fc.gph fd.gph fe.gph ff.gph ,altshrink scheme(s1mono)       graphregion(fcolor(white)) row(2)                       
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)
(note:  named style normarlsize not found in class gsize, default attributes used)

. 
. graph export "$path\fig_a4.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a4.pdf saved as PDF format

. 
. erase fa.gph

. erase fb.gph

. erase fc.gph

. erase fd.gph

. erase fe.gph

. erase ff.gph

. 
. 
. 
. 
. 
. 
. 
.  *************************************************************
. **** Table A-5
. **************************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
.  
.  
. ****Note: The code for results in Columns (1) and (2) is included in the R script file titled "Table A5".
. 
. 
. ****************************Panel A: Health Insurance*******************************************************
.  **health,2014
. use "CFPS_2014.dta", clear     

. 
. logit trust_cadre_dummy health_insurance logfincome age male school_yr hukou rural ccp

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9467.9897  
Iteration 2:  Log likelihood = -9467.6369  
Iteration 3:  Log likelihood = -9467.6369  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(8)    = 328.59
                                                        Prob > chi2   = 0.0000
Log likelihood = -9467.6369                             Pseudo R2     = 0.0171

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
 health_insurance |   .2499994   .0686552     3.64   0.000     .1154378     .384561
       logfincome |  -.0220613   .0146965    -1.50   0.133     -.050866    .0067434
              age |   .0095462   .0012327     7.74   0.000     .0071302    .0119622
             male |  -.0313339   .0357799    -0.88   0.381    -.1014612    .0387933
        school_yr |  -.0206435   .0044128    -4.68   0.000    -.0292924   -.0119945
            hukou |   .1232046   .1023575     1.20   0.229    -.0774123    .3238216
            rural |   .3349614   .0367746     9.11   0.000     .2628844    .4070383
              ccp |   .2898099   .0694004     4.18   0.000     .1537876    .4258322
            _cons |  -1.129093   .2073749    -5.44   0.000     -1.53554   -.7226457
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5A.doc",keep(health_insurance) se  bdec(3) sdec(3) nocons replace
Table_A5A.doc
dir : seeout

. 
. logit trust_cadre_dummy health_insurance logfincome age male school_yr hukou rural ccp log_pop_10_13 log_gdp_pc_10_13 pro_rural

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood =  -9430.759  
Iteration 2:  Log likelihood =  -9430.114  
Iteration 3:  Log likelihood =  -9430.114  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(11)   = 403.64
                                                        Prob > chi2   = 0.0000
Log likelihood = -9430.114                              Pseudo R2     = 0.0210

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
 health_insurance |   .2414949    .068934     3.50   0.000     .1063867     .376603
       logfincome |  -.0083546   .0148442    -0.56   0.574    -.0374487    .0207395
              age |   .0106725   .0012512     8.53   0.000     .0082202    .0131248
             male |  -.0395689   .0359006    -1.10   0.270    -.1099327     .030795
        school_yr |   -.017123    .004484    -3.82   0.000    -.0259115   -.0083345
            hukou |   .0760645   .1029516     0.74   0.460     -.125717    .2778459
            rural |   .2593209   .0379644     6.83   0.000     .1849121    .3337297
              ccp |   .2777321   .0696171     3.99   0.000      .141285    .4141791
    log_pop_10_13 |    .009791   .0392557     0.25   0.803    -.0671488    .0867308
 log_gdp_pc_10_13 |   .0866828   .0550093     1.58   0.115    -.0211334     .194499
        pro_rural |    1.21609   .1927623     6.31   0.000      .838283    1.593897
            _cons |   -2.85461   .6898952    -4.14   0.000     -4.20678   -1.502441
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5A.doc", keep(health_insurance) se  bdec(3) sdec(3) nocons append
Table_A5A.doc
dir : seeout

. 
.  **health,2016
. use "CFPS_2016", clear

. 
. logit trust_cadre_dummy health_insurance logfincome age male school_yr hukou rural ccp

Iteration 0:  Log likelihood = -8788.2897  
Iteration 1:  Log likelihood = -8639.5062  
Iteration 2:  Log likelihood = -8639.0886  
Iteration 3:  Log likelihood = -8639.0886  

Logistic regression                                     Number of obs = 13,477
                                                        LR chi2(8)    = 298.40
                                                        Prob > chi2   = 0.0000
Log likelihood = -8639.0886                             Pseudo R2     = 0.0170

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
 health_insurance |   .2752755    .072806     3.78   0.000     .1325783    .4179727
       logfincome |   -.007582   .0185571    -0.41   0.683    -.0439533    .0287893
              age |   .0149862   .0013198    11.36   0.000     .0123995    .0175729
             male |   .0168954   .0374131     0.45   0.652     -.056433    .0902239
        school_yr |   .0088287   .0045748     1.93   0.054    -.0001377    .0177951
            hukou |   .1234117   .1030467     1.20   0.231    -.0785561    .3253795
            rural |   .2747236   .0389544     7.05   0.000     .1983744    .3510727
              ccp |   .4117718   .0664612     6.20   0.000     .2815102    .5420334
            _cons |  -1.861639    .245988    -7.57   0.000    -2.343766   -1.379511
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5A.doc", keep(health_insurance) se  bdec(3) sdec(3) nocons append
Table_A5A.doc
dir : seeout

. 
. logit trust_cadre_dummy health_insurance logfincome age male school_yr hukou rural ccp log_pop log_gdp_pc pro_rural

Iteration 0:  Log likelihood = -8788.2897  
Iteration 1:  Log likelihood = -8639.3288  
Iteration 2:  Log likelihood = -8638.9095  
Iteration 3:  Log likelihood = -8638.9094  

Logistic regression                                     Number of obs = 13,477
                                                        LR chi2(11)   = 298.76
                                                        Prob > chi2   = 0.0000
Log likelihood = -8638.9094                             Pseudo R2     = 0.0170

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
 health_insurance |   .2732101   .0729264     3.75   0.000     .1302769    .4161433
       logfincome |  -.0063247   .0187152    -0.34   0.735    -.0430058    .0303564
              age |   .0151307    .001343    11.27   0.000     .0124985    .0177629
             male |   .0158776    .037453     0.42   0.672    -.0575289     .089284
        school_yr |   .0092557   .0046331     2.00   0.046     .0001749    .0183365
            hukou |   .1196924   .1032469     1.16   0.246    -.0826678    .3220526
            rural |   .2699561    .039803     6.78   0.000     .1919436    .3479686
              ccp |    .410679   .0665042     6.18   0.000     .2803331     .541025
          log_pop |   .0014121   .0392816     0.04   0.971    -.0755784    .0784026
       log_gdp_pc |  -.0212536   .0498598    -0.43   0.670     -.118977    .0764699
        pro_rural |  -.0037297   .1784848    -0.02   0.983    -.3535534    .3460939
            _cons |  -1.659517   .6654499    -2.49   0.013    -2.963775   -.3552591
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5A.doc", keep(health_insurance) se  bdec(3) sdec(3) nocons append
Table_A5A.doc
dir : seeout

. 
. 
. 
. ****************************Panel B:Pension Insurance*******************************************************
. 
. ***pension, 2014
.  use "CFPS_2014.dta",clear

. logit  trust_cadre_dummy pension_insurance logfincome  age  male school_yr hukou rural ccp

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9470.5786  
Iteration 2:  Log likelihood = -9470.3218  
Iteration 3:  Log likelihood = -9470.3218  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(8)    = 323.22
                                                        Prob > chi2   = 0.0000
Log likelihood = -9470.3218                             Pseudo R2     = 0.0168

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
pension_insurance |   .1562266   .0545174     2.87   0.004     .0493744    .2630788
       logfincome |  -.0193039   .0146984    -1.31   0.189    -.0481122    .0095044
              age |   .0078586   .0014344     5.48   0.000     .0050472    .0106699
             male |  -.0303188   .0357754    -0.85   0.397    -.1004372    .0397996
        school_yr |  -.0199825   .0044187    -4.52   0.000    -.0286429    -.011322
            hukou |   .1549961   .1019547     1.52   0.128    -.0448316    .3548237
            rural |      .3415   .0367075     9.30   0.000     .2695546    .4134455
              ccp |   .3023364   .0694634     4.35   0.000     .1661907    .4384821
            _cons |  -.9134958   .2034126    -4.49   0.000    -1.312177   -.5148145
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5B.doc",keep(pension_insurance) se  bdec(3) sdec(3) nocons replace
Table_A5B.doc
dir : seeout

. 
. logit  trust_cadre_dummy pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9433.0085  
Iteration 2:  Log likelihood = -9432.5066  
Iteration 3:  Log likelihood = -9432.5066  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(11)   = 398.85
                                                        Prob > chi2   = 0.0000
Log likelihood = -9432.5066                             Pseudo R2     = 0.0207

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
pension_insurance |   .1525079   .0547108     2.79   0.005     .0452767    .2597391
       logfincome |  -.0056665   .0148484    -0.38   0.703    -.0347688    .0234358
              age |   .0090323   .0014507     6.23   0.000     .0061889    .0118756
             male |   -.038704   .0358967    -1.08   0.281    -.1090603    .0316523
        school_yr |  -.0164052   .0044899    -3.65   0.000    -.0252053   -.0076051
            hukou |   .1056528   .1025545     1.03   0.303    -.0953503    .3066559
            rural |   .2645615   .0379236     6.98   0.000     .1902327    .3388903
              ccp |   .2895237    .069681     4.15   0.000     .1529513     .426096
    log_pop_10_13 |   .0120971   .0392331     0.31   0.758    -.0647984    .0889926
 log_gdp_pc_10_13 |   .0789478   .0549904     1.44   0.151    -.0288315     .186727
        pro_rural |   1.201033    .192646     6.23   0.000     .8234536    1.578612
            _cons |  -2.571315   .6880624    -3.74   0.000    -3.919893   -1.222738
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5B.doc",keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A5B.doc
dir : seeout

. 
. **pension, 2016
. use "CFPS_2016",clear

. logit  trust_cadre_dummy pension_insurance logfincome  age  male school_yr hukou rural ccp 

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8657.0632  
Iteration 2:  Log likelihood = -8656.7355  
Iteration 3:  Log likelihood = -8656.7355  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(8)    = 294.01
                                                        Prob > chi2   = 0.0000
Log likelihood = -8656.7355                             Pseudo R2     = 0.0167

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
pension_insurance |    .161699   .0561515     2.88   0.004     .0516442    .2717539
       logfincome |  -.0016903    .018572    -0.09   0.927    -.0380908    .0347102
              age |   .0130809   .0015319     8.54   0.000     .0100783    .0160834
             male |   .0178642   .0374065     0.48   0.633    -.0554512    .0911797
        school_yr |   .0086657    .004572     1.90   0.058    -.0002953    .0176268
            hukou |   .1456742   .1020481     1.43   0.153    -.0543363    .3456847
            rural |   .2863289   .0388452     7.37   0.000     .2101936    .3624641
              ccp |   .4268167    .066375     6.43   0.000      .296724    .5569094
            _cons |  -1.634658   .2413338    -6.77   0.000    -2.107663   -1.161652
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5B.doc",keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A5B.doc
dir : seeout

. 
. logit  trust_cadre_dummy pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop  log_gdp_pc pro_rural 

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8656.6674  
Iteration 2:  Log likelihood =  -8656.338  
Iteration 3:  Log likelihood =  -8656.338  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(11)   = 294.80
                                                        Prob > chi2   = 0.0000
Log likelihood = -8656.338                              Pseudo R2     = 0.0167

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
pension_insurance |   .1627864   .0561785     2.90   0.004     .0526785    .2728943
       logfincome |   .0000441   .0187328     0.00   0.998    -.0366716    .0367597
              age |   .0132768   .0015482     8.58   0.000     .0102424    .0163112
             male |   .0164045   .0374444     0.44   0.661    -.0569852    .0897943
        school_yr |   .0093041     .00463     2.01   0.044     .0002294    .0183788
            hukou |    .139774    .102267     1.37   0.172    -.0606657    .3402138
            rural |    .279297   .0397235     7.03   0.000     .2014404    .3571536
              ccp |   .4252174   .0664198     6.40   0.000      .295037    .5553978
          log_pop |   .0036365   .0392259     0.09   0.926    -.0732448    .0805178
       log_gdp_pc |  -.0348707   .0497533    -0.70   0.483    -.1323853    .0626439
        pro_rural |  -.0215549   .1780713    -0.12   0.904    -.3705682    .3274584
            _cons |  -1.301868    .660867    -1.97   0.049    -2.597143   -.0065924
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5B.doc",keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A5B.doc
dir : seeout

. 
. 
. 
. ****************************Panel C: NRPP*******************************************************
. 
. 
. 
. ***nrps, 2014
.  use "CFPS_2014.dta",clear

. logit  trust_cadre_dummy NRPP logfincome  age  male school_yr hukou ccp if rural==1

Iteration 0:  Log likelihood = -5548.6759  
Iteration 1:  Log likelihood = -5478.1011  
Iteration 2:  Log likelihood = -5478.0598  
Iteration 3:  Log likelihood = -5478.0598  

Logistic regression                                     Number of obs =  8,152
                                                        LR chi2(7)    = 141.23
                                                        Prob > chi2   = 0.0000
Log likelihood = -5478.0598                             Pseudo R2     = 0.0127

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             NRPP |   .1779426   .0767463     2.32   0.020     .0275226    .3283626
       logfincome |   .0075453   .0182383     0.41   0.679    -.0282012    .0432918
              age |   .0105544   .0017957     5.88   0.000     .0070349    .0140739
             male |     .00186   .0474221     0.04   0.969    -.0910856    .0948056
        school_yr |   -.019571   .0059748    -3.28   0.001    -.0312814   -.0078605
            hukou |   .1629122   .1652394     0.99   0.324     -.160951    .4867753
              ccp |   .3321519    .104318     3.18   0.001     .1276925    .5366114
            _cons |  -.9957338   .2717714    -3.66   0.000    -1.528396   -.4630716
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5C.doc",keep(NRPP) se  bdec(3) sdec(3) nocons replace
Table_A5C.doc
dir : seeout

. 
. logit  trust_cadre_dummy NRPP logfincome  age  male school_yr hukou ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural if rural==1

Iteration 0:  Log likelihood = -5548.6759  
Iteration 1:  Log likelihood = -5459.1176  
Iteration 2:  Log likelihood = -5459.0242  
Iteration 3:  Log likelihood = -5459.0242  

Logistic regression                                     Number of obs =  8,152
                                                        LR chi2(10)   = 179.30
                                                        Prob > chi2   = 0.0000
Log likelihood = -5459.0242                             Pseudo R2     = 0.0162

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             NRPP |   .1694127   .0769948     2.20   0.028     .0185058    .3203197
       logfincome |   .0161084   .0184064     0.88   0.381    -.0199675    .0521843
              age |   .0115593    .001815     6.37   0.000      .008002    .0151166
             male |  -.0067061   .0475863    -0.14   0.888    -.0999736    .0865613
        school_yr |  -.0156814   .0060703    -2.58   0.010    -.0275789   -.0037838
            hukou |   .1378394   .1658071     0.83   0.406    -.1871364    .4628153
              ccp |   .3096806   .1046167     2.96   0.003     .1046356    .5147256
    log_pop_10_13 |  -.0282546   .0547024    -0.52   0.605    -.1354695    .0789602
 log_gdp_pc_10_13 |   .0103444   .0762168     0.14   0.892    -.1390378    .1597267
        pro_rural |   .9794812   .2836621     3.45   0.001     .4235137    1.535449
            _cons |   -1.61842   .9814216    -1.65   0.099    -3.541971    .3051315
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5C.doc",keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A5C.doc
dir : seeout

. 
. *nrps, 2016
. use "CFPS_2016",clear

. outreg2 using "Table_A5C.doc",keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A5C.doc
dir : seeout

. 
. logit  trust_cadre_dummy NRPP logfincome  age  male school_yr hukou  ccp log_pop  log_gdp_pc pro_rural if rural==1

Iteration 0:  Log likelihood = -4946.7701  
Iteration 1:  Log likelihood = -4872.9699  
Iteration 2:  Log likelihood = -4872.8852  
Iteration 3:  Log likelihood = -4872.8852  

Logistic regression                                     Number of obs =  7,426
                                                        LR chi2(10)   = 147.77
                                                        Prob > chi2   = 0.0000
Log likelihood = -4872.8852                             Pseudo R2     = 0.0149

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             NRPP |   .1985324   .0849123     2.34   0.019     .0321073    .3649575
       logfincome |  -.0308159   .0241108    -1.28   0.201    -.0780722    .0164405
              age |    .014455   .0019624     7.37   0.000     .0106087    .0183013
             male |   .0151477    .050229     0.30   0.763    -.0832992    .1135947
        school_yr |   .0216812   .0062744     3.46   0.001     .0093836    .0339788
            hukou |   .1746425   .1743768     1.00   0.317    -.1671296    .5164147
              ccp |   .5005374   .0986753     5.07   0.000     .3071373    .6939375
          log_pop |  -.0415352   .0540673    -0.77   0.442    -.1475051    .0644348
       log_gdp_pc |   .0413702   .0722125     0.57   0.567    -.1001638    .1829042
        pro_rural |   .0611402    .279132     0.22   0.827    -.4859485     .608229
            _cons |  -1.418405   .9833949    -1.44   0.149    -3.345823    .5090138
-----------------------------------------------------------------------------------

. outreg2 using "Table_A5C.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A5C.doc
dir : seeout

. 
. 
. 
. 
. ********************************************************************************************
. *** Figure A-5
. ********************************************************************************************** */
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. use "boot_list_direct",clear

. 
. 
. ****health_insurance
. twoway (kdensity list_coef_health, width(0.05) lpattern(solid)) (kdensity direct_coef_health if direct_coef_health!=0 ,  width(0.05)), scheme(s2mono)  ///
>     title("Estimated Coefficients of Health Insurance") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("Estimated Coefficients",size(small)  height(5)) ytitle("Density", height(5))   xline(0, lcolor(red) lpattern(dash)) ylab(,nogrid)  

.         
.         
. graph save "f1", replace        
(file f1.gph not found)
file f1.gph saved

.         
. twoway (kdensity list_pvalue_health, width(0.05) lpattern(solid)) (kdensity direct_pvalue_health,  width(0.05)), scheme(s2mono)  ///
>     title("P-value of Health Insurance") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("P-value",size(small)  height(5)) ytitle("Density", height(5))   xline(0.1, lcolor(red) lpattern(dash)) ylab(,nogrid)  xlabel(0(0.1)1)

.         
. graph save "f2", replace        
(file f2.gph not found)
file f2.gph saved

. 
. 
.         
. *****pension            
. 
. twoway (kdensity list_coef_pension, width(0.05) lpattern(solid)) (kdensity direct_coef_pension,  width(0.05)), scheme(s2mono)  ///
>     title("Estimated Coefficients of Pension Insurance") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("Estimated Coefficients",size(small)  height(5)) ytitle("Density", height(5))   xline(0, lcolor(red) lpattern(dash)) ylab(,nogrid)  

.         
.         
. graph save "f3", replace        
(file f3.gph not found)
file f3.gph saved

.         
. twoway (kdensity list_pvalue_pension, width(0.05) lpattern(solid)) (kdensity direct_pvalue_pension,  width(0.05)), scheme(s2mono)  ///
>     title("P-value of Pension Insurance") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("P-value",size(small)  height(5)) ytitle("Density", height(5))   xline(0.1, lcolor(red) lpattern(dash)) ylab(,nogrid)  xlabel(0(0.1)1)

.         
. graph save "f4", replace        
(file f4.gph not found)
file f4.gph saved

. 
. 
. 
. 
. ***NRPP
. 
. 
. twoway (kdensity list_coef_nrpp, width(0.05) lpattern(solid)) (kdensity direct_coef_nrpp if direct_coef_nrpp!=0 ,  width(0.05)), scheme(s2mono)  ///
>     title("Estimated Coefficients of NRPP") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("Estimated Coefficients",size(small)  height(5)) ytitle("Density", height(5))   xline(0, lcolor(red) lpattern(dash)) ylab(,nogrid)  

.         
.         
. graph save "f5", replace        
(file f5.gph not found)
file f5.gph saved

.         
. twoway (kdensity list_pvalue_nrpp, width(0.05) lpattern(solid)) (kdensity direct_pvalue_nrpp,  width(0.05)), scheme(s2mono)  ///
>     title("P-value of NRPP") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("P-value",size(small)  height(5)) ytitle("Density", height(5))   xline(0.1, lcolor(red) lpattern(dash)) ylab(,nogrid)  xlabel(0(0.1)1)

.         
. graph save "f6", replace        
(file f6.gph not found)
file f6.gph saved

. 
. 
. graph combine f1.gph f2.gph f3.gph f4.gph f5.gph f6.gph  ,altshrink     graphregion(fcolor(white)) row(3)

. 
. graph export "$path\fig_a5.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a5.pdf saved as PDF format

. 
. 
. erase f1.gph

. erase f2.gph

. erase f3.gph

. erase f4.gph

. erase f5.gph

. erase f6.gph

. 
. 
. 
. 
.   *************************************************************
. **** Table A-6
. ************************************************************** 
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.  use "boot_list_direct",clear

.  
.  
. 
. local varlist   health pension nrpp income corrup

. local labellist "Health Insurance" ///
>                 "Pension Insurance" ///
>                 "NRPP" ///
>                 "Income" ///
>                 "Corruption Investigation"

. 
. local nvars : word count `varlist'

. 
. 
. matrix T2 = J(2*`nvars', 5, .)

. 
. local r = 1   

. 
. 
. foreach v of local varlist {
  2. 
.     ttest direct_coef_`v' == list_coef_`v'
  3. 
.     scalar mu1  = r(mu_1)
  4.     scalar mu2  = r(mu_2)
  5.     scalar diff = mu1 - mu2
  6.     scalar tval = r(t)
  7.     scalar pval = r(p)
  8. 
.     scalar se1  = r(sd_1) / sqrt(r(N_1))
  9.     scalar se2  = r(sd_2) / sqrt(r(N_2))
 10. 
. 
.     matrix T2[`r',1] = mu1
 11.     matrix T2[`r',2] = mu2
 12.     matrix T2[`r',3] = diff
 13.     matrix T2[`r',4] = tval
 14.     matrix T2[`r',5] = pval
 15. 
.     local ++r
 16.     matrix T2[`r',1] = se1
 17.     matrix T2[`r',2] = se2
 18. 
.     local ++r
 19. }

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |   1,000    .9917566    .0359872    1.138014    .9211375    1.062376
list_c~h |   1,000   -.2782009     .005131     .162257   -.2882697   -.2681321
---------+--------------------------------------------------------------------
    diff |   1,000    1.269958    .0364902    1.153922    1.198351    1.341564
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_he~h - list_coef_health)       t =  34.8027
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |     999    1.783074    .0440333    1.391757    1.696665    1.869482
list_c~n |     999   -.0493589    .0033475    .1058047   -.0559279   -.0427899
---------+--------------------------------------------------------------------
    diff |     999    1.832433    .0442056    1.397205    1.745686    1.919179
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_pe~n - list_coef_pens~n)       t =  41.4525
 H0: mean(diff) = 0                              Degrees of freedom =      998

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
d~f_nrpp |   1,000    .8834416    .0376038    1.189136    .8096501    .9572331
l~f_nrpp |   1,000    .0783484    .0043809    .1385357    .0697516    .0869452
---------+--------------------------------------------------------------------
    diff |   1,000    .8050932    .0381151    1.205304    .7302985     .879888
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_nrpp - list_coef_nrpp)         t =  21.1227
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |   1,000   -.1269096    .0045464    .1437698   -.1358312    -.117988
list_c~e |   1,000   -.0301725    .0004976    .0157353   -.0311489    -.029196
---------+--------------------------------------------------------------------
    diff |   1,000   -.0967371    .0045681    .1444559   -.1057013    -.087773
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_in~e - list_coef_income)       t = -21.1767
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |   1,000    1.378617    .0291142    .9206719    1.321485    1.435749
list_c.. |   1,000   -.0160554    .0022876    .0723397   -.0205444   -.0115664
---------+--------------------------------------------------------------------
    diff |   1,000    1.394673    .0291621    .9221867    1.337447    1.451899
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_co~p - list_coef_corrup)       t =  47.8248
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
. 
. matrix rownames T2 = ///
>     "Health Insurance" "" ///
>     "Pension Insurance" "" ///
>     "NRPP" "" ///
>     "Income" "" ///
>     "Corruption Investigation" ""

. 
. matrix colnames T2 = "Direct" "List" "Direct-List" "t-statistics" "p-value"

. 
. esttab matrix(T2, fmt(%9.3f)) using "Table_A6.doc", replace mlabels(none) label
(file Table_A6.doc not found)
(output written to Table_A6.doc)

. 
. 
. 
.   
.   
.  *************************************************************
. **** Table A-7
. **************************************************************
. 
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

. **CFPS 2014, 7/10=1
. 
.  use "CFPS_2014.dta",clear

. 
. 
. quietly logit  trust_cadre_dummy7 health_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc", keep(health_insurance) se  bdec(3) sdec(3) nocons replace
Table_A7.doc
dir : seeout

. 
. quietly logit  trust_cadre_dummy7 pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc", keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. quietly logit  trust_cadre_dummy7  NRPP logfincome  age  male school_yr hukou ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1

. outreg2 using "Table_A7.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. 
. 
. **CFPS 2014, 5/10=1
. 
. quietly logit  trust_cadre_dummy5 health_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc", keep(health_insurance) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. quietly logit  trust_cadre_dummy5 pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc", keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. quietly logit  trust_cadre_dummy5 NRPP logfincome  age  male school_yr hukou ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1

. outreg2 using "Table_A7.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. 
. 
. 
. 
. **CFPS, 104,, 0-11
. 
. quietly reg trust_cadre0 health_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc",keep(health_insurance) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. quietly reg trust_cadre0 pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

. outreg2 using "Table_A7.doc", keep(pension_insurance) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. quietly reg trust_cadre0 NRPP logfincome  age  male school_yr hukou ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1

. outreg2 using "Table_A7.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A7.doc
dir : seeout

. 
. 
. 
. 
. 
. 
.  *************************************************************
. ****Table A-8
. **************************************************************
.  cd "$path\data\MI"
F:\Dropbox\Research\PSRM\replication file\data\MI

.  
.  
. ***CFPS 2014
. clear

. 
. 
. 
. miest CFPS logit  trust_cadre_dummy health_insurance  logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 19752
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
health_insurance |  .24151 .0599469    4.029       454    0.000
logfincome | -.00364    .0128944     -0.282       1247    0.778
     age |   .01073     .0011226      9.560        199    0.000
    male |   -.0443     .0323536     -1.369        653    0.171
school_yr | -.00906     .0042606     -2.126         40    0.040
   hukou |   .00975     .0864738      0.113        391    0.910
   rural |   .26007     .0330925      7.859       5931    0.000
     ccp |   .23637     .0666806      3.545       1372    0.000
log_pop_10_13 |  .02828 .0363894      0.777        364    0.438
log_gdp_pc_10_13 |  .04284 .0496102    0.863       740    0.388
pro_rural |   1.121      .176554      6.349        574    0.000
   _cons |   -2.475     .6400414     -3.867        303    0.000
---------------------------------------------------------------


. 
. miest CFPS logit  trust_cadre_dummy  pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 19752
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
pension_insurance |  .18667 .0498081    3.748     2828    0.000
logfincome |  -.0005    .0129242     -0.039       1150    0.969
     age |   .00882     .0012824      6.877        285    0.000
    male |  -.04154     .0323007     -1.286        687    0.199
school_yr | -.00859      .004212     -2.039         42    0.048
   hukou |   .04596     .0860027      0.534        387    0.593
   rural |   .26436     .0331152      7.983       5075    0.000
     ccp |   .25033     .0666457      3.756       1455    0.000
log_pop_10_13 |   .0297 .0363132      0.818        378    0.414
log_gdp_pc_10_13 |  .03683  .049678    0.741       705    0.459
pro_rural |  1.1048     .1768802      6.246        539    0.000
   _cons |  -2.2095       .64303     -3.436        270    0.001
---------------------------------------------------------------


. 
. miest CFPS  logit  trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp log_pop_10_13  log_gdp_pc_10_13  pro_rural if rural==1,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 11509
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
    NRPP |   .20359     .0691736      2.943       5302    0.003
logfincome |  .01581    .0156132      1.013       4131    0.311
     age |   .01055      .001533      6.884        579    0.000
    male |  -.02053     .0435818     -0.471        268    0.638
school_yr | -.00845     .0051379     -1.645         77    0.104
   hukou |   .05545     .1272137      0.436        738    0.663
     ccp |   .29754     .1003794      2.964       1188    0.003
log_pop_10_13 |  .00682  .050277      0.136        276    0.892
log_gdp_pc_10_13 | -.00548 .0700135   -0.078       245    0.938
pro_rural |  .97926     .2575344      3.802        380    0.000
   _cons |  -1.5635      .923095     -1.694        151    0.092
---------------------------------------------------------------


. 
. 
. 
. 
. ***CFPS 2016
. 
. clear

. 
. miest CFPnew logit  trust_cadre_dummy health_insurance  logfincome  age  male school_yr hukou rural ccp log_pop  log_gdp_pc pro_rural,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 15968
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
health_insurance |  .24054 .0643638    3.737     39093    0.000
logfincome | -.00344    .0174245     -0.198       1861    0.843
     age |   .01443     .0012119     11.909       5331    0.000
    male |    .0316     .0345753      0.914      27613    0.361
school_yr |  .00517     .0038181      1.353        682    0.177
   hukou |   .09814      .093602      1.048        317    0.295
   rural |   .24186     .0365067      6.625    1366836    0.000
     ccp |   .41983     .0614996      6.827     265993    0.000
 log_pop |   .01894     .0362557      0.522     185916    0.601
log_gdp_pc | -.01121    .0458066     -0.245     143454    0.807
pro_rural |  .06023       .16378      0.368     221266    0.713
   _cons |  -1.8092     .6125221     -2.954      48309    0.003
---------------------------------------------------------------


. 
. miest CFPnew logit  trust_cadre_dummy  pension_insurance logfincome  age  male school_yr hukou rural ccp log_pop  log_gdp_pc pro_rural,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 15968
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
pension_insurance |  .21828 .0525417    4.154    52915    0.000
logfincome |  .00346     .017463      0.198       1809    0.843
     age |   .01185     .0013912      8.516      22337    0.000
    male |    .0363     .0346003      1.049      28357    0.294
school_yr |  .00528     .0038179      1.383        680    0.167
   hukou |   .12106     .0928659      1.304        347    0.193
   rural |   .24702     .0364762      6.772    1561644    0.000
     ccp |   .42853      .061489      6.969     306273    0.000
 log_pop |   .01801     .0362445      0.497     198310    0.619
log_gdp_pc | -.02309    .0457703     -0.504     125367    0.614
pro_rural |  .04104     .1636481      0.251     219802    0.802
   _cons |  -1.4654     .6098946     -2.403      32468    0.016
---------------------------------------------------------------


. 
. miest CFPnew logit  trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp log_pop  log_gdp_pc  pro_rural if rural==1,nsets(10)

Multiple Imputation Estimates

Model: logit
Dependent Variable: trust_cadre_dummy

Number of Observations: 8638
---------------------------------------------------------------
         |      Coef.   Std. Err.       t          Df     P>|t|
---------------------------------------------------------------
    NRPP |   .21622     .0784139      2.757      62539    0.006
logfincome | -.03318    .0225737     -1.470       6026    0.142
     age |   .01305     .0018336      7.117       4263    0.000
    male |    .0416     .0468654      0.888      23822    0.375
school_yr |  .01603     .0058754      2.728        306    0.007
   hukou |   .16666     .1798537      0.927         58    0.358
     ccp |   .49326     .0931657      5.294    1986881    0.000
 log_pop |  -.01621     .0501626     -0.323     162817    0.747
log_gdp_pc |  .00824    .0669859      0.123      90213    0.902
pro_rural |  .03918     .2598027      0.151     100142    0.880
   _cons |  -1.1091      .918179     -1.208       7897    0.227
---------------------------------------------------------------


. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. **********************************************************
. ****Figure A-6
. **********************************************************
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
. 
. ***The code estimating the results in this figure is included in R script titled "Figure A6"
. 
. **health insurance
. 
. clear

. 
.  set_defaults graphics
-> set graphics on
-> set scheme s2color
-> set printcolor automatic
-> set copycolor automatic
(preferences reset)

.  
.  
. import excel "results.xlsx", sheet("health") firstrow
(7 vars, 7 obs)

. 
. 
. twoway ///
> (rcap low95 high95 row, horiz lcolor(black)) ///  
> (scatter row coefficient  if group ==1,  mcolor(black) msize(small) lpattern(solid)  )  /// 
> (scatter row coefficient if group ==2, mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> (scatter row coefficient if group ==3,  mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///
> (scatter  row coefficient if group ==4,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///  
> (scatter row coefficient if group ==5,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> , leg(off)  /// 
> ylabel(2 "Maximum Likelihood" 3 "Top-coding Error" 4 "Uniform Error" 5"Robust Maximum Likelihood" 6 "Nonlinear Least Square" , angle(0)  labsize(small)) ///
> xtitle("Estimated Coefficients",height(5) size(small))  title("Health Insurance", size(small)  color(black))  xlabel(-2(1)2)  ///
> ytitle("",height(5))  xline(0, lpattern(dash) lcolor(red)) graphregion(fcolor(white)) ylab(,nogrid)  fxsize(55)

. 
. 
.  graph save "f1" , replace
(file f1.gph not found)
file f1.gph saved

. 
. 
. 
. 
. 
. **pension insurance
. 
. clear

. 
. 
. 
. import excel "results.xlsx", sheet("pension") firstrow
(7 vars, 7 obs)

. 
. 
. twoway ///
> (rcap low95 high95 row, horiz lcolor(black)) /// 
> (scatter row coefficient  if group ==1,   mcolor(black) msize(small) lpattern(solid)  )  /// 
> (scatter row coefficient if group ==2,  mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> (scatter row coefficient if group ==3,  mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///
> (scatter  row coefficient if group ==4,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///  
> (scatter row coefficient if group ==5,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> , leg(off)  /// 
> ylabel(2 "Maximum Likelihood" 3 "Top-coding Error" 4 "Uniform Error" 5"Robust Maximum Likelihood" 6 "Nonlinear Least Square" , angle(0)  labsize(small)) ///
> xtitle("Estimated Coefficients",height(5) size(small)) title("Peansion Insurance", size(small)  color(black)) xlabel(-2(1)2) ///
> ytitle("",height(5))  xline(0, lpattern(dash) lcolor(red)) graphregion(fcolor(white)) ylab(,nogrid) yscale(lstyle(none)) yscale(range( .)) ylabel(0) 

. 
. 
.  graph save "f2" ,   replace
(file f2.gph not found)
file f2.gph saved

. 
. 
. **NRPS
. 
. clear

. 
. 
. 
. import excel "results.xlsx", sheet("nrps") firstrow
(7 vars, 7 obs)

. 
. 
. twoway ///
> (rcap low95 high95 row, horiz lcolor(black)) /// 
> (scatter row coefficient  if group ==1,   mcolor(black) msize(small) lpattern(solid)  )  /// 
> (scatter row coefficient if group ==2,  mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> (scatter row coefficient if group ==3, mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///
> (scatter  row coefficient if group ==4,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) ///  
> (scatter row coefficient if group ==5,   mcolor(black) msize(small) lpattern(solid) lcolor(black)) /// 
> , leg(off)  /// 
> ylabel(2 "Maximum Likelihood" 3 "Top-coding Error" 4 "Uniform Error" 5"Robust Maximum Likelihood" 6 "Nonlinear Least Square" , angle(0)  labsize(small)) ///
> xtitle("Estimated Coefficients",height(5) size(small))  title("NRPS", size(small) color(black))   xlabel(-2(1)2)   ///
> ytitle("",height(5))  xline(0, lpattern(dash) lcolor(red)) graphregion(fcolor(white)) ylab(,nogrid) yscale(lstyle(none)) yscale(range( .)) ylabel(0) 

. 
. 
.  graph save "f3" , replace
(file f3.gph not found)
file f3.gph saved

. 
.  
.  graph combine f1.gph f2.gph f3.gph, ycommon graphregion(fcolor(white) lcolor(white) ifcolor(white) ilcolor(white))     graphregion(fcolor(white)) row(1)  iscale(0.75)

.  
.  
. graph export "$path\fig_a6.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a6.pdf saved as PDF format

. 
. 
. erase f1.gph

. erase f2.gph

. erase f3.gph

.  
.  
.  
.  
.  
.  
.  
.  *************************************************************
. **** Table A-9
. **************************************************************
. 
. 
. ****The code for results in Columns (1) and (2) is included in the R script file titled "Table A9".
. 
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

. 
. ***CFPS, 2014
.  use "CFPS_2014.dta",clear

. 
. logit  trust_cadre_dummy  logcorrup_num logfincome  age  male school_yr hukou rural ccp

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9474.3051  
Iteration 2:  Log likelihood = -9474.0523  
Iteration 3:  Log likelihood = -9474.0523  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(8)    = 315.76
                                                        Prob > chi2   = 0.0000
Log likelihood = -9474.0523                             Pseudo R2     = 0.0164

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
    logcorrup_num |  -.0197532   .0228842    -0.86   0.388    -.0646054    .0250991
       logfincome |  -.0193659   .0147154    -1.32   0.188    -.0482076    .0094758
              age |   .0100407   .0012275     8.18   0.000     .0076348    .0124466
             male |  -.0319988   .0357652    -0.89   0.371    -.1020974    .0380998
        school_yr |  -.0204142   .0044129    -4.63   0.000    -.0290634   -.0117649
            hukou |    .150461    .101996     1.48   0.140    -.0494474    .3503695
            rural |   .3417874   .0367708     9.30   0.000      .269718    .4138567
              ccp |   .2943264    .069406     4.24   0.000     .1582931    .4303597
            _cons |  -.9211636   .2104603    -4.38   0.000    -1.333658    -.508669
-----------------------------------------------------------------------------------

. outreg2 using "Table_A9.doc", keep(logcorrup_num) se  bdec(3) sdec(3) nocons replace
Table_A9.doc
dir : seeout

. 
. logit  trust_cadre_dummy  logcorrup_num logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural  

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9434.4463  
Iteration 2:  Log likelihood = -9433.9391  
Iteration 3:  Log likelihood = -9433.9391  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(11)   = 395.99
                                                        Prob > chi2   = 0.0000
Log likelihood = -9433.9391                             Pseudo R2     = 0.0206

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
    logcorrup_num |   .0656237   .0296888     2.21   0.027     .0074346    .1238128
       logfincome |  -.0067687    .014835    -0.46   0.648    -.0358447    .0223073
              age |   .0112216   .0012459     9.01   0.000     .0087798    .0136635
             male |   -.041505   .0358971    -1.16   0.248     -.111862     .028852
        school_yr |   -.016313   .0044921    -3.63   0.000    -.0251175   -.0075086
            hukou |   .0945672   .1026064     0.92   0.357    -.1065376     .295672
            rural |   .2631519   .0379484     6.93   0.000     .1887744    .3375293
              ccp |    .284591     .06962     4.09   0.000     .1481383    .4210438
    log_pop_10_13 |  -.0359877   .0451629    -0.80   0.426    -.1245053    .0525299
 log_gdp_pc_10_13 |      .0558    .056244     0.99   0.321    -.0544363    .1660363
        pro_rural |   1.233886   .1932607     6.38   0.000     .8551023     1.61267
            _cons |  -2.302838   .7070032    -3.26   0.001    -3.688539   -.9171377
-----------------------------------------------------------------------------------

. outreg2 using "Table_A9.doc",  keep(logcorrup_num) se  bdec(3) sdec(3) nocons append
Table_A9.doc
dir : seeout

. 
. **CFPS, 2016
. use "CFPS_2016.dta",clear

. 
. logit  trust_cadre_dummy  logcorrup_num logfincome  age  male school_yr hukou rural ccp 

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8658.7397  
Iteration 2:  Log likelihood = -8658.4301  
Iteration 3:  Log likelihood = -8658.4301  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(8)    = 290.62
                                                        Prob > chi2   = 0.0000
Log likelihood = -8658.4301                             Pseudo R2     = 0.0165

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
    logcorrup_num |    .050455   .0228549     2.21   0.027     .0056601    .0952499
       logfincome |  -.0077173   .0185539    -0.42   0.677    -.0440822    .0286476
              age |   .0153217   .0013144    11.66   0.000     .0127456    .0178978
             male |   .0136433   .0373733     0.37   0.715    -.0596071    .0868936
        school_yr |   .0086784   .0045704     1.90   0.058    -.0002795    .0176363
            hukou |    .139184   .1021016     1.36   0.173    -.0609316    .3392995
            rural |   .2870444   .0388315     7.39   0.000      .210936    .3631528
              ccp |   .4280242   .0663762     6.45   0.000     .2979292    .5581192
            _cons |  -1.828798   .2507829    -7.29   0.000    -2.320324   -1.337273
-----------------------------------------------------------------------------------

. outreg2 using "Table_A9.doc",  keep(logcorrup_num) se  bdec(3) sdec(3) nocons append
Table_A9.doc
dir : seeout

. 
. logit  trust_cadre_dummy  logcorrup_num logfincome  age  male school_yr hukou rural ccp log_pop  log_gdp_pc pro_rural  

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8656.5133  
Iteration 2:  Log likelihood = -8656.1922  
Iteration 3:  Log likelihood = -8656.1922  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(11)   = 295.10
                                                        Prob > chi2   = 0.0000
Log likelihood = -8656.1922                             Pseudo R2     = 0.0168

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
    logcorrup_num |   .0831405   .0283108     2.94   0.003     .0276523    .1386287
       logfincome |  -.0037291   .0186725    -0.20   0.842    -.0403265    .0328683
              age |   .0157695   .0013391    11.78   0.000      .013145     .018394
             male |   .0105147   .0374226     0.28   0.779    -.0628323    .0838617
        school_yr |   .0101261   .0046377     2.18   0.029     .0010364    .0192157
            hukou |   .1267805   .1023894     1.24   0.216    -.0738989      .32746
            rural |   .2714944   .0398372     6.82   0.000     .1934149    .3495739
              ccp |   .4233645     .06642     6.37   0.000     .2931837    .5535453
          log_pop |    -.05797   .0447844    -1.29   0.196    -.1457458    .0298057
       log_gdp_pc |  -.0372044   .0499288    -0.75   0.456    -.1350631    .0606543
        pro_rural |   .0722529   .1816675     0.40   0.691     -.283809    .4283147
            _cons |  -1.286068   .6631942    -1.94   0.052    -2.585905    .0137685
-----------------------------------------------------------------------------------

. outreg2 using "Table_A9.doc", keep(logcorrup_num) se  bdec(3) sdec(3) nocons append
Table_A9.doc
dir : seeout

. 
. 
. 
. 
. 
.  *************************************************************
. **** Table A-10
. **************************************************************
. 
. ****The code for results in Columns (1) and (2) is included in the R script file titled "Table A10".
. 
.  cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

. 
. ***CFPS,2014, raw
.  use "CFPS_2014.dta",clear

. 
. logit  trust_cadre_dummy  logfincome  age  male school_yr hukou rural ccp 

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9474.6737  
Iteration 2:  Log likelihood = -9474.4246  
Iteration 3:  Log likelihood = -9474.4246  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(7)    = 315.02
                                                        Prob > chi2   = 0.0000
Log likelihood = -9474.4246                             Pseudo R2     = 0.0164

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
       logfincome |  -.0201341   .0146865    -1.37   0.170     -.048919    .0086509
              age |   .0099917   .0012262     8.15   0.000     .0075884    .0123949
             male |  -.0318972   .0357639    -0.89   0.372    -.1019932    .0381987
        school_yr |  -.0205273   .0044111    -4.65   0.000    -.0291729   -.0118816
            hukou |   .1502047   .1019773     1.47   0.141    -.0496671    .3500765
            rural |   .3439339   .0366874     9.37   0.000     .2720278    .4158399
              ccp |   .2959819   .0693818     4.27   0.000     .1599961    .4319676
            _cons |  -.9709503   .2023936    -4.80   0.000    -1.367634   -.5742662
-----------------------------------------------------------------------------------

. outreg2 using "Table_A10.doc", keep(logfincome) se  bdec(3) sdec(3) nocons replace
Table_A10.doc
dir : seeout

. 
. logit  trust_cadre_dummy  logfincome  age  male school_yr hukou rural ccp log_pop_10_13  log_gdp_pc_10_13 pro_rural 

Iteration 0:  Log likelihood = -9631.9338  
Iteration 1:  Log likelihood = -9436.8914  
Iteration 2:  Log likelihood =  -9436.389  
Iteration 3:  Log likelihood =  -9436.389  

Logistic regression                                     Number of obs = 14,481
                                                        LR chi2(10)   = 391.09
                                                        Prob > chi2   = 0.0000
Log likelihood = -9436.389                              Pseudo R2     = 0.0203

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
       logfincome |   -.006484   .0148353    -0.44   0.662    -.0355606    .0225927
              age |    .011111   .0012445     8.93   0.000     .0086719    .0135502
             male |  -.0402219   .0358856    -1.12   0.262    -.1105564    .0301126
        school_yr |  -.0169702   .0044819    -3.79   0.000    -.0257546   -.0081858
            hukou |   .1009087   .1025794     0.98   0.325    -.1001431    .3019606
            rural |   .2669264   .0379027     7.04   0.000     .1926384    .3412144
              ccp |   .2834856   .0696053     4.07   0.000     .1470618    .4199094
    log_pop_10_13 |   .0134625   .0392091     0.34   0.731    -.0633859    .0903109
 log_gdp_pc_10_13 |    .081224    .054972     1.48   0.140    -.0265193    .1889672
        pro_rural |   1.209508   .1925928     6.28   0.000      .832033    1.586983
            _cons |  -2.663486   .6871469    -3.88   0.000    -4.010269   -1.316702
-----------------------------------------------------------------------------------

. outreg2 using "Table_A10.doc", keep(logfincome) se  bdec(3) sdec(3) nocons append
Table_A10.doc
dir : seeout

. 
. 
. ***CFPS,2016, raw
.  use "CFPS_2016.dta",clear

. 
. logit  trust_cadre_dummy  logfincome  age  male school_yr hukou rural ccp 

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8661.1807  
Iteration 2:  Log likelihood = -8660.8774  
Iteration 3:  Log likelihood = -8660.8774  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(7)    = 285.72
                                                        Prob > chi2   = 0.0000
Log likelihood = -8660.8774                             Pseudo R2     = 0.0162

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
       logfincome |  -.0050582   .0185228    -0.27   0.785    -.0413622    .0312458
              age |   .0153504   .0013139    11.68   0.000     .0127751    .0179256
             male |   .0135616    .037367     0.36   0.717    -.0596763    .0867995
        school_yr |   .0086372   .0045686     1.89   0.059    -.0003171    .0175915
            hukou |   .1434502    .102102     1.40   0.160     -.056666    .3435664
            rural |   .2872568   .0388281     7.40   0.000     .2111551    .3633586
              ccp |   .4257253   .0663506     6.42   0.000     .2956805    .5557701
            _cons |  -1.674516   .2408613    -6.95   0.000    -2.146596   -1.202437
-----------------------------------------------------------------------------------

. outreg2 using "Table_A10.doc", keep(logfincome) se  bdec(3) sdec(3) nocons append
Table_A10.doc
dir : seeout

. 
. logit  trust_cadre_dummy  logfincome  age  male school_yr hukou rural ccp log_pop  log_gdp_pc pro_rural 

Iteration 0:  Log likelihood = -8803.7399  
Iteration 1:  Log likelihood = -8660.8369  
Iteration 2:  Log likelihood = -8660.5319  
Iteration 3:  Log likelihood = -8660.5318  

Logistic regression                                     Number of obs = 13,500
                                                        LR chi2(10)   = 286.42
                                                        Prob > chi2   = 0.0000
Log likelihood = -8660.5318                             Pseudo R2     = 0.0163

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
       logfincome |  -.0035073   .0186798    -0.19   0.851    -.0401191    .0331044
              age |   .0155444   .0013362    11.63   0.000     .0129255    .0181632
             male |   .0121895   .0374061     0.33   0.745    -.0611252    .0855041
        school_yr |   .0092226   .0046265     1.99   0.046     .0001547    .0182904
            hukou |   .1379046    .102322     1.35   0.178    -.0626428    .3384521
            rural |   .2807094   .0397038     7.07   0.000     .2028915    .3585274
              ccp |   .4243021    .066396     6.39   0.000     .2941683    .5544359
          log_pop |   .0053087   .0392048     0.14   0.892    -.0715312    .0821487
       log_gdp_pc |  -.0324173   .0497314    -0.65   0.514    -.1298891    .0650544
        pro_rural |  -.0184666   .1780114    -0.10   0.917    -.3673626    .3304295
            _cons |  -1.377483   .6602857    -2.09   0.037    -2.671619   -.0833465
-----------------------------------------------------------------------------------

. outreg2 using "Table_A10.doc", keep(logfincome) se  bdec(3) sdec(3) nocons append
Table_A10.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. ********************************************************************************************
. *** Figure A-7
. ********************************************************************************************** */
. 
.  use "boot_list_direct",clear

.  
. ****income
. twoway (kdensity list_coef_income, width(0.05) lpattern(solid)) (kdensity direct_coef_income,  width(0.05)), scheme(s2mono)  ///
>     title("Estimated Coefficients of Income") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("Estimated Coefficients",size(small)  height(5)) ytitle("Density", height(5))   xline(0, lcolor(red) lpattern(dash)) ylab(,nogrid)  

.         
.         
. graph save "f1", replace        
(file f1.gph not found)
file f1.gph saved

.         
. twoway (kdensity list_pvalue_income, width(0.05) lpattern(solid)) (kdensity direct_pvalue_income,  width(0.05)), scheme(s2mono)  ///
>     title("P-value of Income") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("P-value",size(small)  height(5)) ytitle("Density", height(5))   xline(0.1, lcolor(red) lpattern(dash)) ylab(,nogrid)  xlabel(0(0.1)1)

.         
. graph save "f2", replace        
(file f2.gph not found)
file f2.gph saved

. 
. 
.         
. *****corruption investigation   
. 
. twoway (kdensity list_coef_corrup, width(0.05) lpattern(solid)) (kdensity direct_coef_corrup,  width(0.05)), scheme(s2mono)  ///
>     title("Estimated Coefficients of Corruption Investigation") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("Estimated Coefficients",size(small)  height(5)) ytitle("Density", height(5))   xline(0, lcolor(red) lpattern(dash)) ylab(,nogrid)   xlabel(-1(1)2)

.         
.         
. graph save "f3", replace        
(file f3.gph not found)
file f3.gph saved

.         
. twoway (kdensity list_pvalue_corrup, width(0.05) lpattern(solid)) (kdensity direct_pvalue_corrup,  width(0.05)), scheme(s2mono)  ///
>     title("P-value of Corruption Investigation") legend(label(1 "List") label(2 "Direct")) graphregion(fcolor(white)) ///
>          xtitle("P-value",size(small)  height(5)) ytitle("Density", height(5))   xline(0.1, lcolor(red) lpattern(dash)) ylab(,nogrid)  xlabel(0(0.1)1)

.         
. graph save "f4", replace        
(file f4.gph not found)
file f4.gph saved

. 
. 
. 
. graph combine f1.gph f2.gph f3.gph f4.gph   ,altshrink  graphregion(fcolor(white)) row(2)

. graph export "$path\fig_a7.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a7.pdf saved as PDF format

. 
. 
. 
. erase f1.gph

. erase f2.gph

. erase f3.gph

. erase f4.gph

. 
. 
. 
. 
. 
. 
.   *************************************************************
. **** Figure A-8
. ************************************************************** 
.  
.  use "boot_list_direct",clear

.  
.  
.   ttest  direct_coef_income ==list_coef_income

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |   1,000   -.1269096    .0045464    .1437698   -.1358312    -.117988
list_c~e |   1,000   -.0301725    .0004976    .0157353   -.0311489    -.029196
---------+--------------------------------------------------------------------
    diff |   1,000   -.0967371    .0045681    .1444559   -.1057013    -.087773
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_in~e - list_coef_income)       t = -21.1767
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

.   
.   ttest  direct_coef_corrup ==list_coef_corrup

Paired t test
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
direct.. |   1,000    1.378617    .0291142    .9206719    1.321485    1.435749
list_c.. |   1,000   -.0160554    .0022876    .0723397   -.0205444   -.0115664
---------+--------------------------------------------------------------------
    diff |   1,000    1.394673    .0291621    .9221867    1.337447    1.451899
------------------------------------------------------------------------------
     mean(diff) = mean(direct_coef_co~p - list_coef_corrup)       t =  47.8248
 H0: mean(diff) = 0                              Degrees of freedom =      999

 Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. 
.   
. clear

. 
. 
. 
. import excel "results.xlsx", sheet("diff2") firstrow
(7 vars, 5 obs)

. 
. 
. twoway ///
> (rcap low95 high95 row, vert lcolor(black)) /// 
> (scatter coef row if group == 1, lcolor(black) mcolor(black) msize(small) lpattern(solid)) /// 
> (scatter coef row if group == 2, lcolor(black) mcolor(black) msize(small) lpattern(solid)) /// 
> , leg(off) /// 
> xlabel(2 "Income" 3 "Corruption Investigation" , angle(0) labsize(small)) /// 
> ytitle("Direct-List Difference", height(5) size(small)) /// 
> title("", size(small) color(black)) /// 
> ylabel(-0.5(0.5)1.5) xtitle("", height(5)) /// 
> graphregion(fcolor(white)) ylab(, nogrid)  yline(0, lpattern(dash) lcolor(red))

. 
. graph export "$path\fig_a8.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a8.pdf saved as PDF format

. 
. 
. 
. 
. 
. 
. 
. 
.   *************************************************************
. **** Figure A9
. **************************************************************
. 
.  
.   use "CFPS_2014.dta",clear

. 
.  quietly reg  trust_cadre_dummy  i.age  logfincome  male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1&age<70&age>50

.  
.  estimate store m1

.  
.  
. 
. 
. coefplot m1,  mcolor(black) msymbol(S) lpatt(solid)  lwidth(vvthin) ciopts(lpatt(shortdash) lcol(black))  ci(95)   ///
>            drop(_cons logfincome  male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural )  yline(0)   ytitle("Effect of Age Dummy on Local Political Trust", height(5) size
> (small)) ///            
>            vertical  legend(off) graphregion(fcolor(white)) bfcolor(none)   ///
>                      ylabel(-0.4(0.2)0.4, nogrid angle(vertical) labsize(small)) title("")   xline(9,lpattern(dash)) ///
>                         coeflabels(52.age="52" 53.age="53" 54.age="54" 55.age="55" 56.age="56" 57.age="57" 58.age="58" 59.age="59"  ///
>                         60.age="60" 61.age="61" 62.age="62" 63.age="63" 64.age="64" 65.age="65" 66.age="66" 67.age="67" 68.age="68" 69.age="69" , labsize(small) ) 

.                 
. 
. graph export "$path\fig_a9.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a9.pdf saved as PDF format

. 
.  
.  
.  
.  
. 
.  
.   *************************************************************
. **** Table A14
. **************************************************************
. 
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.   use "CFPS_2014.dta",clear

. 
. 
.    reg  trust_cadre_dummy  i.age    if rural==1&age<70&age>50

      Source |       SS           df       MS      Number of obs   =     3,103
-------------+----------------------------------   F(18, 3084)     =      1.50
       Model |  6.72496181        18   .37360899   Prob > F        =    0.0783
    Residual |  765.639202     3,084  .248261739   R-squared       =    0.0087
-------------+----------------------------------   Adj R-squared   =    0.0029
       Total |  772.364164     3,102  .248989092   Root MSE        =    .49826

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |
         52  |  -.0329567   .0443061    -0.74   0.457    -.1198293    .0539158
         53  |  -.0250393   .0561768    -0.45   0.656     -.135187    .0851084
         54  |   .0099737   .0513921     0.19   0.846    -.0907926      .11074
         55  |   .0087429   .0565159     0.15   0.877    -.1020696    .1195555
         56  |   .0605179   .0492324     1.23   0.219    -.0360136    .1570495
         57  |   .0686125   .0465036     1.48   0.140    -.0225687    .1597936
         58  |  -.0180046   .0489057    -0.37   0.713    -.1138956    .0778864
         59  |   .0518142   .0493161     1.05   0.293    -.0448815    .1485098
         60  |    .017878    .046272     0.39   0.699    -.0728491     .108605
         61  |   .0227868   .0484385     0.47   0.638    -.0721881    .1177617
         62  |   .0689389   .0482145     1.43   0.153    -.0255969    .1634746
         63  |   .1247066   .0510796     2.44   0.015     .0245532    .2248601
         64  |   .0181169   .0532542     0.34   0.734    -.0863004    .1225342
         65  |   .0041349   .0496592     0.08   0.934    -.0932336    .1015034
         66  |   .0245984   .0553704     0.44   0.657    -.0839682     .133165
         67  |   .0826585   .0550633     1.50   0.133     -.025306     .190623
         68  |   .1405225   .0587799     2.39   0.017     .0252708    .2557741
         69  |   .1157156   .0610343     1.90   0.058    -.0039564    .2353876
             |
       _cons |   .4337349   .0315759    13.74   0.000     .3718231    .4956468
------------------------------------------------------------------------------

.  outreg2 using "Table_A14.doc", se  bdec(3) sdec(3) nocons replace
Table_A14.doc
dir : seeout

.  
.   reg  trust_cadre_dummy  i.age  logfincome  male school_yr hukou ccp   if rural==1&age<70&age>50

      Source |       SS           df       MS      Number of obs   =     2,854
-------------+----------------------------------   F(23, 2830)     =      1.35
       Model |  7.71129764        23   .33527381   Prob > F        =    0.1229
    Residual |   702.82304     2,830  .248347364   R-squared       =    0.0109
-------------+----------------------------------   Adj R-squared   =    0.0028
       Total |  710.534338     2,853  .249048138   Root MSE        =    .49834

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |
         52  |  -.0351115   .0467331    -0.75   0.453     -.126746    .0565229
         53  |   -.036613   .0594404    -0.62   0.538    -.1531639    .0799378
         54  |   .0210401   .0538804     0.39   0.696    -.0846086    .1266889
         55  |  -.0017646   .0593954    -0.03   0.976    -.1182272    .1146981
         56  |   .0475724    .052006     0.91   0.360    -.0544011    .1495459
         57  |   .0765202    .049593     1.54   0.123    -.0207219    .1737623
         58  |   -.040669   .0512146    -0.79   0.427    -.1410907    .0597527
         59  |    .032748   .0523967     0.63   0.532    -.0699916    .1354875
         60  |  -.0032345   .0495193    -0.07   0.948    -.1003321    .0938631
         61  |   .0206228   .0509253     0.40   0.686    -.0792317    .1204772
         62  |   .0481706   .0507119     0.95   0.342    -.0512653    .1476066
         63  |   .1095181   .0542205     2.02   0.043     .0032024    .2158337
         64  |   .0043991   .0560081     0.08   0.937    -.1054217      .11422
         65  |   .0050964   .0527602     0.10   0.923     -.098356    .1085487
         66  |   .0081679   .0585024     0.14   0.889    -.1065438    .1228796
         67  |   .0657938   .0580339     1.13   0.257    -.0479992    .1795867
         68  |   .1506934   .0618603     2.44   0.015     .0293976    .2719893
         69  |   .0906813   .0647162     1.40   0.161    -.0362144    .2175771
             |
  logfincome |   .0059282   .0074272     0.80   0.425     -.008635    .0204915
        male |  -.0194139   .0205136    -0.95   0.344    -.0596369    .0208092
   school_yr |  -.0015194   .0024119    -0.63   0.529    -.0062487    .0032099
       hukou |  -.0840524   .0904849    -0.93   0.353    -.2614755    .0933708
         ccp |   .0736976   .0373568     1.97   0.049     .0004483    .1469468
       _cons |   .4773433   .1249935     3.82   0.000     .2322556     .722431
------------------------------------------------------------------------------

.  outreg2 using "Table_A14.doc",  drop(logfincome  male school_yr hukou ccp) se  bdec(3) sdec(3) nocons append
Table_A14.doc
dir : seeout

.  
.   reg  trust_cadre_dummy  i.age  logfincome  male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1&age<70&age>50

      Source |       SS           df       MS      Number of obs   =     2,854
-------------+----------------------------------   F(26, 2827)     =      1.95
       Model |  12.5369842        26  .482191699   Prob > F        =    0.0027
    Residual |  697.997354     2,827   .24690391   R-squared       =    0.0176
-------------+----------------------------------   Adj R-squared   =    0.0086
       Total |  710.534338     2,853  .249048138   Root MSE        =    .49689

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
             age |
             52  |  -.0390616   .0466096    -0.84   0.402    -.1304539    .0523306
             53  |   -.033293   .0592941    -0.56   0.575     -.149557    .0829709
             54  |   .0245885   .0537338     0.46   0.647     -.080773      .12995
             55  |    .010144   .0592902     0.17   0.864    -.1061124    .1264005
             56  |    .052596   .0518866     1.01   0.311    -.0491434    .1543353
             57  |   .0799042   .0495158     1.61   0.107    -.0171866    .1769949
             58  |  -.0404873   .0510759    -0.79   0.428    -.1406372    .0596625
             59  |   .0379471     .05227     0.73   0.468    -.0645441    .1404384
             60  |  -.0009569   .0494114    -0.02   0.985    -.0978429     .095929
             61  |   .0190508   .0508479     0.37   0.708     -.080652    .1187536
             62  |    .056361   .0506085     1.11   0.266    -.0428724    .1555943
             63  |   .1132148   .0540893     2.09   0.036     .0071564    .2192732
             64  |   .0097694   .0559107     0.17   0.861    -.0998606    .1193993
             65  |   .0143189   .0526812     0.27   0.786    -.0889785    .1176163
             66  |   .0103251   .0584524     0.18   0.860    -.1042886    .1249388
             67  |    .070323   .0579701     1.21   0.225    -.0433449     .183991
             68  |    .153524   .0617547     2.49   0.013     .0324351     .274613
             69  |   .0883844   .0646579     1.37   0.172    -.0383971     .215166
                 |
      logfincome |   .0057542   .0074618     0.77   0.441    -.0088768    .0203852
            male |  -.0219876   .0205076    -1.07   0.284     -.062199    .0182237
       school_yr |  -.0010984   .0024155    -0.45   0.649    -.0058347     .003638
           hukou |  -.0873287   .0902274    -0.97   0.333    -.2642469    .0895895
             ccp |   .0702147   .0372662     1.88   0.060    -.0028569    .1432864
   log_pop_10_13 |   .0099109   .0224539     0.44   0.659    -.0341167    .0539386
log_gdp_pc_10_13 |  -.0355064   .0314343    -1.13   0.259    -.0971428    .0261301
       pro_rural |   .1651302   .1174483     1.41   0.160    -.0651628    .3954232
           _cons |   .6867266   .4193751     1.64   0.102    -.1355856    1.509039
----------------------------------------------------------------------------------

.  outreg2 using "Table_A14.doc",  drop( logfincome  male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural)  se  bdec(3) sdec(3) nocons append
Table_A14.doc
dir : seeout

. 
. 
. 
. 
.  *************************************************************
. **** Table A-15
. **************************************************************
. 
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.   use "CFPS_panel.dta",clear

.  
. 
.  sort pid year

. mi xtset pid year

Panel variable: pid (unbalanced)
 Time variable: year, 2014 to 2016, but with gaps
         Delta: 1 unit

. 
. 
. xtlogit trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp i.year if rural==1,fe
note: multiple positive outcomes within groups encountered.
note: 7,845 groups (11,385 obs) omitted because of all positive or
      all negative outcomes.
note: male omitted because of no within-group variance.

Iteration 0:  Log likelihood =  -1481.275  
Iteration 1:  Log likelihood = -1469.3503  
Iteration 2:  Log likelihood = -1469.3476  
Iteration 3:  Log likelihood = -1469.3476  

Conditional fixed-effects logistic regression        Number of obs    =  4,278
Group variable: pid                                  Number of groups =  2,139

                                                     Obs per group:
                                                                  min =      2
                                                                  avg =    2.0
                                                                  max =      2

                                                     LR chi2(7)       =  26.59
Log likelihood = -1469.3476                          Prob > chi2      = 0.0004

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             NRPP |    .040071   .1240372     0.32   0.747    -.2030373    .2831794
       logfincome |   .0446016   .0314717     1.42   0.156    -.0170819     .106285
              age |   .0386478   .1712334     0.23   0.821    -.2969635    .3742591
             male |          0  (omitted)
        school_yr |   .1033957   .0399926     2.59   0.010     .0250116    .1817798
            hukou |   .1548124   .6735615     0.23   0.818    -1.165344    1.474969
              ccp |   -.169912   .2674614    -0.64   0.525    -.6941268    .3543028
                  |
             year |
            2016  |   -.227765   .3568168    -0.64   0.523     -.927113     .471583
-----------------------------------------------------------------------------------

. outreg2 using "Table_A15.doc", keep(NRPP) se  bdec(3) sdec(3) nocons replace
Table_A15.doc
dir : seeout

.  
.  
. xtlogit trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp log_pop  log_gdp_pc pro_rural i.year if rural==1,fe
note: multiple positive outcomes within groups encountered.
note: 7,845 groups (11,385 obs) omitted because of all positive or
      all negative outcomes.
note: male omitted because of no within-group variance.

Iteration 0:  Log likelihood = -1480.9345  
Iteration 1:  Log likelihood = -1460.8451  
Iteration 2:  Log likelihood = -1460.8389  
Iteration 3:  Log likelihood = -1460.8389  

Conditional fixed-effects logistic regression        Number of obs    =  4,278
Group variable: pid                                  Number of groups =  2,139

                                                     Obs per group:
                                                                  min =      2
                                                                  avg =    2.0
                                                                  max =      2

                                                     LR chi2(10)      =  43.61
Log likelihood = -1460.8389                          Prob > chi2      = 0.0000

-----------------------------------------------------------------------------------
trust_cadre_dummy | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             NRPP |    .003437    .125161     0.03   0.978    -.2418741     .248748
       logfincome |   .0405971    .031591     1.29   0.199    -.0213202    .1025144
              age |   .0207718   .1724217     0.12   0.904    -.3171686    .3587122
             male |          0  (omitted)
        school_yr |   .0941392    .040184     2.34   0.019     .0153801    .1728983
            hukou |   .0321601   .6746696     0.05   0.962    -1.290168    1.354488
              ccp |  -.1886154   .2684644    -0.70   0.482     -.714796    .3375652
          log_pop |  -1.479556   .5948877    -2.49   0.013    -2.645515   -.3135976
       log_gdp_pc |  -2.229604   .7223247    -3.09   0.002    -3.645335   -.8138736
        pro_rural |  -.9142046   1.140563    -0.80   0.423    -3.149666    1.321257
                  |
             year |
            2016  |   .1592307   .3815239     0.42   0.676    -.5885424    .9070038
-----------------------------------------------------------------------------------

. outreg2 using "Table_A15.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A15.doc
dir : seeout

.  
. xtreg trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp i.year if rural==1,fe
note: male omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =     15,663
Group variable: pid                             Number of groups  =      9,984

R-squared:                                      Obs per group:
     Within  = 0.0045                                         min =          1
     Between = 0.0139                                         avg =        1.6
     Overall = 0.0115                                         max =          2

                                                F(7, 5672)        =       3.67
corr(u_i, Xb) = -0.2116                         Prob > F          =     0.0006

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        NRPP |   .0040468   .0238854     0.17   0.865    -.0427777    .0508714
  logfincome |   .0086782   .0062378     1.39   0.164    -.0035503    .0209067
         age |   .0100346   .0330882     0.30   0.762    -.0548309    .0749001
        male |          0  (omitted)
   school_yr |   .0166436   .0069612     2.39   0.017     .0029971    .0302901
       hukou |   .0432827   .1251316     0.35   0.729    -.2020231    .2885885
         ccp |  -.0437373    .051056    -0.86   0.392    -.1438266     .056352
             |
        year |
       2016  |  -.0482984   .0688819    -0.70   0.483    -.1833332    .0867365
             |
       _cons |  -.2775805   1.540265    -0.18   0.857    -3.297088    2.741927
-------------+----------------------------------------------------------------
     sigma_u |  .43927247
     sigma_e |   .4332513
         rho |  .50690054   (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(9983, 5672) = 1.41                  Prob > F = 0.0000

. outreg2 using "Table_A15.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A15.doc
dir : seeout

.  
. xtreg trust_cadre_dummy  NRPP logfincome  age  male school_yr hukou ccp log_pop  log_gdp_pc pro_rural i.year if rural==1,fe
note: male omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =     15,663
Group variable: pid                             Number of groups  =      9,984

R-squared:                                      Obs per group:
     Within  = 0.0073                                         min =          1
     Between = 0.0032                                         avg =        1.6
     Overall = 0.0029                                         max =          2

                                                F(10, 5669)       =       4.19
corr(u_i, Xb) = -0.4911                         Prob > F          =     0.0000

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        NRPP |  -.0026731    .023925    -0.11   0.911    -.0495753    .0442291
  logfincome |   .0080058   .0062358     1.28   0.199    -.0042189    .0202304
         age |   .0063091   .0330875     0.19   0.849     -.058555    .0711731
        male |          0  (omitted)
   school_yr |   .0148167   .0069804     2.12   0.034     .0011325     .028501
       hukou |   .0227918   .1251004     0.18   0.855    -.2224529    .2680365
         ccp |  -.0442231   .0509982    -0.87   0.386    -.1441991    .0557529
     log_pop |  -.2240867   .0945127    -2.37   0.018    -.4093678   -.0388056
  log_gdp_pc |  -.4222436   .1354362    -3.12   0.002    -.6877504   -.1567368
   pro_rural |  -.1800341   .2068493    -0.87   0.384    -.5855378    .2254696
             |
        year |
       2016  |   .0242295   .0729027     0.33   0.740    -.1186877    .1671468
             |
       _cons |   5.725265   2.243296     2.55   0.011     1.327546    10.12298
-------------+----------------------------------------------------------------
     sigma_u |  .49016216
     sigma_e |  .43275098
         rho |  .56196669   (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(9983, 5669) = 1.41                  Prob > F = 0.0000

. outreg2 using "Table_A15.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A15.doc
dir : seeout

. 
. 
. 
. 
. 
.    *************************************************************
. ****Table A-16
. **************************************************************
.  
.  
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.  
.  use "CFPS_10_12.dta",clear

. 
.  
. 
. 
. reghdfe support_gov_dummy treat   if rural==1 & age>=60, ab(countyID year) cluster(cntygb)
(dropped 4 singleton observations)
(converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      8,535
Absorbing 2 HDFE groups                           F(   1,    153) =       0.05
Statistics robust to heteroskedasticity           Prob > F        =     0.8275
                                                  R-squared       =     0.0927
                                                  Adj R-squared   =     0.0760
                                                  Within R-sq.    =     0.0000
Number of clusters (cntygb)  =        154         Root MSE        =     0.4551

                               (Std. err. adjusted for 154 clusters in cntygb)
------------------------------------------------------------------------------
             |               Robust
support_go~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0055065   .0252205     0.22   0.827    -.0443188    .0553319
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
    countyID |            0             154            154 *   | 
        year |            1               2              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. outreg2 using "Table_A16.doc", keep(treat) se  bdec(3) sdec(3) nocons replace
Table_A16.doc
dir : seeout

. 
. reghdfe support_gov_dummy treat   logfincome  age  male school_yr  ccp  if rural==1 & age>=60, ab(countyID year) cluster(cntygb)
(dropped 3 singleton observations)
(converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      7,767
Absorbing 2 HDFE groups                           F(   6,    152) =       2.53
Statistics robust to heteroskedasticity           Prob > F        =     0.0233
                                                  R-squared       =     0.0920
                                                  Adj R-squared   =     0.0731
                                                  Within R-sq.    =     0.0017
Number of clusters (cntygb)  =        153         Root MSE        =     0.4564

                               (Std. err. adjusted for 153 clusters in cntygb)
------------------------------------------------------------------------------
             |               Robust
support_go~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0067642   .0250743     0.27   0.788     -.042775    .0563034
  logfincome |   .0024354   .0055386     0.44   0.661    -.0085073     .013378
         age |   .0018176   .0007965     2.28   0.024     .0002439    .0033913
        male |   .0016403   .0124584     0.13   0.895    -.0229737    .0262542
   school_yr |   .0033331   .0020076     1.66   0.099    -.0006334    .0072995
         ccp |     .04519   .0244049     1.85   0.066    -.0030267    .0934067
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
    countyID |            0             153            153 *   | 
        year |            1               2              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. outreg2 using "Table_A16.doc", keep(treat) se  bdec(3) sdec(3) nocons append
Table_A16.doc
dir : seeout

. 
. 
. reghdfe support_gov_dummy treat   if rural==1 & age>=60&dup!=0, ab(countyID year) cluster(cntygb)
(dropped 4 singleton observations)
(converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      7,127
Absorbing 2 HDFE groups                           F(   1,    148) =       0.14
Statistics robust to heteroskedasticity           Prob > F        =     0.7111
                                                  R-squared       =     0.0975
                                                  Adj R-squared   =     0.0780
                                                  Within R-sq.    =     0.0000
Number of clusters (cntygb)  =        149         Root MSE        =     0.4550

                               (Std. err. adjusted for 149 clusters in cntygb)
------------------------------------------------------------------------------
             |               Robust
support_go~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0093737   .0252633     0.37   0.711    -.0405498    .0592971
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
    countyID |            0             149            149 *   | 
        year |            1               2              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. outreg2 using "Table_A16.doc", keep(treat) se  bdec(3) sdec(3) nocons append
Table_A16.doc
dir : seeout

. 
. reghdfe support_gov_dummy treat   logfincome  age  male school_yr  ccp  if rural==1 & age>=60&dup!=0, ab(countyID year) cluster(cntygb)
(dropped 5 singleton observations)
(converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      6,498
Absorbing 2 HDFE groups                           F(   6,    147) =       3.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0037
                                                  R-squared       =     0.0961
                                                  Adj R-squared   =     0.0742
                                                  Within R-sq.    =     0.0027
Number of clusters (cntygb)  =        148         Root MSE        =     0.4565

                               (Std. err. adjusted for 148 clusters in cntygb)
------------------------------------------------------------------------------
             |               Robust
support_go~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |   .0054953   .0245693     0.22   0.823    -.0430594    .0540499
  logfincome |   .0012427   .0061138     0.20   0.839    -.0108396     .013325
         age |   .0027613   .0009053     3.05   0.003     .0009722    .0045504
        male |   -.001197   .0140102    -0.09   0.932    -.0288845    .0264905
   school_yr |   .0039226   .0021215     1.85   0.066      -.00027    .0081151
         ccp |   .0537754    .030608     1.76   0.081    -.0067131    .1142639
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------------+
 Absorbed FE |  Num. Coefs.  =   Categories  -   Redundant     | 
-------------+-------------------------------------------------|
    countyID |            0             148            148 *   | 
        year |            1               2              1     | 
---------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation

. outreg2 using "Table_A16.doc", keep(treat) se  bdec(3) sdec(3) nocons append
Table_A16.doc
dir : seeout

. 
. 
. 
. 
. 
.    *************************************************************
. ****Table A-17
. **************************************************************
.  
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.  use "CFPS_2014",clear

.  
.  
.  **full sample
. 
.   ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp    if rural==1,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   8152
------------------------------------------------------------------------------
        NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    agedummy |   .4585306   .0097952    46.81   0.000     .4393296    .4777316
  logfincome |   .0076858   .0023132     3.32   0.001     .0031513    .0122203
         age |   .0008375   .0002789     3.00   0.003     .0002907    .0013843
        male |    -.00724   .0059771    -1.21   0.226    -.0189566    .0044766
   school_yr |  -.0022427   .0007552    -2.97   0.003    -.0037231   -.0007623
       hukou |   .0309703   .0200582     1.54   0.123     -.008349    .0702896
         ccp |  -.0365354   .0133316    -2.74   0.006    -.0626687   -.0104022
       _cons |  -.1198902    .033824    -3.54   0.000    -.1861938   -.0535866
------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  8144) =  2191.36
  Prob > F      =   0.0000
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  8144) =  2191.36
  Prob > F      =   0.0000



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  8144)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  8144)
NRPP         |    2191.36    0.0000 |     2193.51   0.0000 |     2191.36

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=1728.43  P-val=0.0000

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                    2191.36

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,8144)=      0.86     P-val=0.3545
Anderson-Rubin Wald test           Chi-sq(1)=      0.86     P-val=0.3542
Stock-Wright LM S statistic        Chi-sq(1)=      0.86     P-val=0.3542

Number of observations               N  =       8152
Number of regressors                 K  =          8
Number of endogenous regressors      K1 =          1
Number of instruments                L  =          8
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     8152
                                                      F(  7,  8144) =    19.73
                                                      Prob > F      =   0.0000
Total (centered) SS     =  1987.282507                Centered R2   =   0.0173
Total (uncentered) SS   =         3433                Uncentered R2 =   0.4311
Residual SS             =  1952.985659                Root MSE      =    .4895

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        NRPP |   .0375827   .0405546     0.93   0.354    -.0419028    .1170683
  logfincome |   .0017174   .0043737     0.39   0.695    -.0068549    .0102896
         age |   .0025993   .0005532     4.70   0.000      .001515    .0036835
        male |   .0003563   .0113422     0.03   0.975    -.0218741    .0225867
   school_yr |  -.0047344   .0014408    -3.29   0.001    -.0075583   -.0019105
       hukou |   .0361955    .038058     0.95   0.342    -.0383969    .1107879
         ccp |    .081124   .0253256     3.20   0.001     .0314868    .1307612
       _cons |   .2612092   .0649101     4.02   0.000     .1339878    .3884306
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):        1728.431
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):             2191.358
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons replace
Table_A17.doc
dir : seeout

. 
.    ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural  if rural==1,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   8152
----------------------------------------------------------------------------------
            NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        agedummy |   .4589345   .0098011    46.82   0.000     .4397218    .4781472
      logfincome |   .0076186   .0023244     3.28   0.001     .0030623     .012175
             age |   .0008967   .0002794     3.21   0.001      .000349    .0014445
            male |  -.0080582   .0059787    -1.35   0.178    -.0197779    .0036615
       school_yr |  -.0018549   .0007662    -2.42   0.016    -.0033569   -.0003529
           hukou |   .0300676   .0200498     1.50   0.134    -.0092352    .0693704
             ccp |  -.0385262    .013335    -2.89   0.004    -.0646663   -.0123861
   log_pop_10_13 |   .0113322    .006896     1.64   0.100    -.0021857    .0248501
log_gdp_pc_10_13 |   -.025491   .0095532    -2.67   0.008    -.0442177   -.0067642
       pro_rural |  -.0203194   .0351775    -0.58   0.564    -.0892763    .0486375
           _cons |   .0788991   .1227867     0.64   0.521    -.1617942    .3195925
----------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  8141) =  2192.55
  Prob > F      =   0.0000
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  8141) =  2192.55
  Prob > F      =   0.0000



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  8141)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  8141)
NRPP         |    2192.55    0.0000 |     2195.51   0.0000 |     2192.55

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=1729.67  P-val=0.0000

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                    2192.55

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,8141)=      1.01     P-val=0.3155
Anderson-Rubin Wald test           Chi-sq(1)=      1.01     P-val=0.3151
Stock-Wright LM S statistic        Chi-sq(1)=      1.01     P-val=0.3152

Number of observations               N  =       8152
Number of regressors                 K  =         11
Number of endogenous regressors      K1 =          1
Number of instruments                L  =         11
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     8152
                                                      F( 10,  8141) =    17.71
                                                      Prob > F      =   0.0000
Total (centered) SS     =  1987.282507                Centered R2   =   0.0218
Total (uncentered) SS   =         3433                Uncentered R2 =   0.4337
Residual SS             =  1943.952463                Root MSE      =    .4883

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |   .0406739   .0404819     1.00   0.315     -.038669    .1200169
      logfincome |   .0037877    .004388     0.86   0.388    -.0048127     .012388
             age |   .0027736   .0005548     5.00   0.000     .0016862     .003861
            male |  -.0017014   .0113273    -0.15   0.881    -.0239024    .0204997
       school_yr |  -.0037585   .0014583    -2.58   0.010    -.0066166   -.0009003
           hukou |   .0299724   .0379844     0.79   0.430    -.0444756    .1044204
             ccp |   .0755386   .0252946     2.99   0.003     .0259621    .1251151
   log_pop_10_13 |  -.0066624   .0130982    -0.51   0.611    -.0323343    .0190096
log_gdp_pc_10_13 |   .0017919   .0181091     0.10   0.921    -.0337013    .0372851
       pro_rural |   .2283204   .0666671     3.42   0.001     .0976554    .3589855
           _cons |   .1247718   .2325518     0.54   0.592    -.3310213    .5805649
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):        1729.675
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):             2192.552
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A17.doc
dir : seeout

. 
.    ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp    if rural==1&age<70&age>50,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   2854
------------------------------------------------------------------------------
        NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    agedummy |   .3025396    .025509    11.86   0.000     .2525216    .3525576
  logfincome |   .0137988   .0052926     2.61   0.009     .0034211    .0241765
         age |   .0160852   .0024379     6.60   0.000     .0113049    .0208654
        male |  -.0121667   .0146128    -0.83   0.405    -.0408194    .0164861
   school_yr |  -.0045694   .0017049    -2.68   0.007    -.0079123   -.0012264
       hukou |   .1556817   .0644843     2.41   0.016      .029241    .2821224
         ccp |  -.0470601   .0265851    -1.77   0.077    -.0991881     .005068
       _cons |  -1.125671   .1639689    -6.87   0.000     -1.44718   -.8041608
------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  2846) =   140.66
  Prob > F      =   0.0000
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  2846) =   140.66
  Prob > F      =   0.0000



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  2846)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  2846)
NRPP         |     140.66    0.0000 |      141.06   0.0000 |      140.66

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=134.41   P-val=0.0000

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                     140.66

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,2846)=      0.73     P-val=0.3925
Anderson-Rubin Wald test           Chi-sq(1)=      0.73     P-val=0.3918
Stock-Wright LM S statistic        Chi-sq(1)=      0.73     P-val=0.3919

Number of observations               N  =       2854
Number of regressors                 K  =          8
Number of endogenous regressors      K1 =          1
Number of instruments                L  =          8
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     2854
                                                      F(  7,  2846) =     2.01
                                                      Prob > F      =   0.0503
Total (centered) SS     =  710.5343378                Centered R2   =  -0.0046
Total (uncentered) SS   =         1335                Uncentered R2 =   0.4653
Residual SS             =  713.7826961                Root MSE      =    .5001

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        NRPP |  -.1009085   .1183986    -0.85   0.394    -.3329655    .1311484
  logfincome |   .0062672   .0076174     0.82   0.411    -.0086625    .0211969
         age |    .008827     .00512     1.72   0.085    -.0012081     .018862
        male |  -.0183301   .0205605    -0.89   0.373    -.0586279    .0219677
   school_yr |  -.0022334   .0024888    -0.90   0.370    -.0071115    .0026446
       hukou |  -.0658304   .0926515    -0.71   0.477    -.2474241    .1157632
         ccp |   .0641904   .0376723     1.70   0.088     -.009646    .1380269
       _cons |  -.0147086   .3338063    -0.04   0.965    -.6689571    .6395398
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):         134.414
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              140.662
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A17.doc
dir : seeout

. 
.     ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural   if rural==1&age<70&age>50,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   2854
----------------------------------------------------------------------------------
            NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        agedummy |   .3030355   .0254586    11.90   0.000     .2531164    .3529546
      logfincome |   .0121699   .0053199     2.29   0.022     .0017387    .0226011
             age |   .0162192   .0024364     6.66   0.000      .011442    .0209965
            male |  -.0150096   .0146139    -1.03   0.304    -.0436646    .0136453
       school_yr |  -.0041225   .0017083    -2.41   0.016    -.0074722   -.0007729
           hukou |   .1537971   .0643442     2.39   0.017     .0276312    .2799631
             ccp |  -.0492768   .0265392    -1.86   0.063    -.1013148    .0027612
   log_pop_10_13 |   .0174666   .0159943     1.09   0.275    -.0138951    .0488283
log_gdp_pc_10_13 |  -.0728799   .0224182    -3.25   0.001    -.1168375   -.0289223
       pro_rural |  -.1206749   .0837202    -1.44   0.150    -.2848334    .0434836
           _cons |   -.409004    .326013    -1.25   0.210     -1.04825    .2302419
----------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  2843) =   141.68
  Prob > F      =   0.0000
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  2843) =   141.68
  Prob > F      =   0.0000



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  2843)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  2843)
NRPP         |     141.68    0.0000 |      142.23   0.0000 |      141.68

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=135.48   P-val=0.0000

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                     141.68

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,2843)=      0.83     P-val=0.3632
Anderson-Rubin Wald test           Chi-sq(1)=      0.83     P-val=0.3622
Stock-Wright LM S statistic        Chi-sq(1)=      0.83     P-val=0.3623

Number of observations               N  =       2854
Number of regressors                 K  =         11
Number of endogenous regressors      K1 =          1
Number of instruments                L  =         11
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     2854
                                                      F( 10,  2843) =     3.31
                                                      Prob > F      =   0.0003
Total (centered) SS     =  710.5343378                Centered R2   =   0.0019
Total (uncentered) SS   =         1335                Uncentered R2 =   0.4688
Residual SS             =  709.1596549                Root MSE      =    .4985

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.1068593   .1178569    -0.91   0.365    -.3378546    .1241361
      logfincome |   .0059569   .0076092     0.78   0.434    -.0089568    .0208705
             age |   .0092775   .0051193     1.81   0.070    -.0007561    .0193112
            male |  -.0211808   .0205643    -1.03   0.303    -.0614861    .0191245
       school_yr |    -.00182   .0024767    -0.73   0.462    -.0066743    .0030343
           hukou |  -.0691534   .0923048    -0.75   0.454    -.2500675    .1117607
             ccp |   .0599677   .0376072     1.59   0.111     -.013741    .1336764
   log_pop_10_13 |   .0135802    .022534     0.60   0.547    -.0305856    .0577461
log_gdp_pc_10_13 |  -.0425103   .0324539    -1.31   0.190    -.1061187    .0210981
       pro_rural |   .1517471   .1179643     1.29   0.198    -.0794587    .3829529
           _cons |   .2307099   .4763153     0.48   0.628     -.702851    1.164271
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):         135.480
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              141.683
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A17.doc
dir : seeout

. 
.     ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp     if rural==1&age<65&age>55,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   1488
------------------------------------------------------------------------------
        NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    agedummy |   .1015817    .038356     2.65   0.008     .0263439    .1768195
  logfincome |   .0039652   .0079896     0.50   0.620     -.011707    .0196373
         age |   .0670203   .0076906     8.71   0.000     .0519347    .0821059
        male |  -.0150499   .0212953    -0.71   0.480     -.056822    .0267222
   school_yr |  -.0034072   .0024735    -1.38   0.169    -.0082592    .0014447
       hukou |   .1367057   .0885754     1.54   0.123    -.0370411    .3104524
         ccp |  -.0400511   .0382048    -1.05   0.295    -.1149924    .0348901
       _cons |  -3.973858   .4592314    -8.65   0.000    -4.874672   -3.073044
------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  1480) =     7.01
  Prob > F      =   0.0082
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  1480) =     7.01
  Prob > F      =   0.0082



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  1480)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  1480)
NRPP         |       7.01    0.0082 |        7.05   0.0079 |        7.01

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=7.02     P-val=0.0081

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                       7.01

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,1480)=      0.02     P-val=0.8783
Anderson-Rubin Wald test           Chi-sq(1)=      0.02     P-val=0.8780
Stock-Wright LM S statistic        Chi-sq(1)=      0.02     P-val=0.8780

Number of observations               N  =       1488
Number of regressors                 K  =          8
Number of endogenous regressors      K1 =          1
Number of instruments                L  =          8
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     1488
                                                      F(  7,  1480) =     0.97
                                                      Prob > F      =   0.4549
Total (centered) SS     =  371.1767473                Centered R2   =  -0.0052
Total (uncentered) SS   =          709                Uncentered R2 =   0.4738
Residual SS             =  373.0920298                Root MSE      =    .5007

------------------------------------------------------------------------------
trust_cadr~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        NRPP |   -.077607   .5078887    -0.15   0.879     -1.07305    .9178365
  logfincome |   .0102734   .0108694     0.95   0.345    -.0110302    .0315769
         age |   .0085759   .0432273     0.20   0.843    -.0761479    .0932998
        male |  -.0282456   .0295014    -0.96   0.338    -.0860673    .0295761
   school_yr |  -.0002702    .003783    -0.07   0.943    -.0076847    .0071443
       hukou |  -.0176369   .1381897    -0.13   0.898    -.2884837      .25321
         ccp |   .1150378   .0548516     2.10   0.036     .0075307    .2225449
       _cons |  -.0972374   2.543404    -0.04   0.970    -5.082217    4.887742
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):           7.019
                                                   Chi-sq(1) P-val =    0.0081
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                7.014
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A17.doc
dir : seeout

. 
.      ivreg2   trust_cadre_dummy (NRPP=agedummy )   logfincome  age male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural   if rural==1&age<65&age>55,first

First-stage regressions
-----------------------


First-stage regression of NRPP:

Statistics consistent for homoskedasticity only
Number of obs =                   1488
----------------------------------------------------------------------------------
            NRPP | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        agedummy |   .1004781   .0383681     2.62   0.009     .0252164    .1757398
      logfincome |   .0022943   .0080424     0.29   0.775    -.0134815    .0180702
             age |   .0674187   .0076885     8.77   0.000     .0523372    .0825002
            male |   -.016637   .0213223    -0.78   0.435    -.0584622    .0251883
       school_yr |  -.0030933   .0024858    -1.24   0.214    -.0079693    .0017828
           hukou |    .141069   .0885324     1.59   0.111    -.0325935    .3147315
             ccp |  -.0423383   .0381974    -1.11   0.268    -.1172653    .0325887
   log_pop_10_13 |   .0196428   .0236935     0.83   0.407    -.0268337    .0661194
log_gdp_pc_10_13 |  -.0537966   .0323717    -1.66   0.097    -.1172961    .0097028
       pro_rural |  -.0684507   .1223847    -0.56   0.576     -.308517    .1716156
           _cons |  -3.516301   .6207837    -5.66   0.000    -4.734013   -2.298589
----------------------------------------------------------------------------------
F test of excluded instruments:
  F(  1,  1477) =     6.86
  Prob > F      =   0.0089
Sanderson-Windmeijer multivariate F test of excluded instruments:
  F(  1,  1477) =     6.86
  Prob > F      =   0.0089



Summary results for first-stage regressions
-------------------------------------------

                                           (Underid)            (Weak id)
Variable     | F(  1,  1477)  P-val | SW Chi-sq(  1) P-val | SW F(  1,  1477)
NRPP         |       6.86    0.0089 |        6.91   0.0086 |        6.86

Stock-Yogo weak ID F test critical values for single endogenous regressor:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Sanderson-Windmeijer F statistic.

Underidentification test
Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
Ha: matrix has rank=K1 (identified)
Anderson canon. corr. LM statistic       Chi-sq(1)=6.88     P-val=0.0087

Weak identification test
Ho: equation is weakly identified
Cragg-Donald Wald F statistic                                       6.86

Stock-Yogo weak ID test critical values for K1=1 and L1=1:
                                   10% maximal IV size             16.38
                                   15% maximal IV size              8.96
                                   20% maximal IV size              6.66
                                   25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.

Weak-instrument-robust inference
Tests of joint significance of endogenous regressors B1 in main equation
Ho: B1=0 and orthogonality conditions are valid
Anderson-Rubin Wald test           F(1,1477)=      0.05     P-val=0.8207
Anderson-Rubin Wald test           Chi-sq(1)=      0.05     P-val=0.8201
Stock-Wright LM S statistic        Chi-sq(1)=      0.05     P-val=0.8201

Number of observations               N  =       1488
Number of regressors                 K  =         11
Number of endogenous regressors      K1 =          1
Number of instruments                L  =         11
Number of excluded instruments       L1 =          1

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     1488
                                                      F( 10,  1477) =     1.56
                                                      Prob > F      =   0.1137
Total (centered) SS     =  371.1767473                Centered R2   =  -0.0058
Total (uncentered) SS   =          709                Uncentered R2 =   0.4735
Residual SS             =  373.3135398                Root MSE      =    .5009

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.1160004   .5141565    -0.23   0.822    -1.123729    .8917278
      logfincome |   .0091486   .0108545     0.84   0.399    -.0121258     .030423
             age |   .0119108   .0438533     0.27   0.786    -.0740402    .0978617
            male |  -.0302093   .0297808    -1.01   0.310    -.0885787      .02816
       school_yr |  -.0001837   .0037408    -0.05   0.961    -.0075155    .0071482
           hukou |  -.0062795    .139848    -0.04   0.964    -.2803765    .2678174
             ccp |   .1107569   .0554225     2.00   0.046     .0021309    .2193829
   log_pop_10_13 |   .0251968   .0338068     0.75   0.456    -.0410634    .0914569
log_gdp_pc_10_13 |  -.0378482   .0512372    -0.74   0.460    -.1382711    .0625748
       pro_rural |   .1576558   .1674974     0.94   0.347     -.170633    .4859446
           _cons |  -.1447492   2.415394    -0.06   0.952    -4.878835    4.589336
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):           6.877
                                                   Chi-sq(1) P-val =    0.0087
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                6.858
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: logfincome age male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
------------------------------------------------------------------------------

. outreg2 using "Table_A17.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
Table_A17.doc
dir : seeout

. 
. 
. 
. 
. 
. 
.  *************************************************************
. ****Figure A-10
. **************************************************************
. 
. 
. 
. 
. ** ssc install rdrobust  //**install the package if not installed
.  
.  
.  use "CFPS_2014.dta",clear

.  
. 
. rdplot NRPP agediff if  age<=66&age>=54&rural==1,   c(0) p(1) binselect(qspr)       ///
>                     graph_options(title(,size(medsmall))  xline(0) xlabel(-6[2]6) ylabel(0[0.2]1)  ///
>                      ytitle("Proportion of NRPS Recipient",size(small)) legend(off) ///
>                      xtitle(Age relative to 60,height(5)) ylab(,nogrid)  ///
>                      title("", size(small))  graphregion(color(white)))

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       2313
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1065        1248
   Eff. Number of obs |      1065        1248
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     6.000       6.000
 Number of bins scale |     1.000       1.000

Outcome: NRPP. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        18          40
   Average bin length |     1.000       0.857
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        18          40
  Mimicking Var. bins |        39          44
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------


.                                         
. graph export "$path\fig_a10.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a10.pdf saved as PDF format

. 
.  
. 
. 
.                                 
.  *************************************************************
. ****Figure A-11
. **************************************************************
. 
.  use "CFPS_2014.dta",clear

.  
. forval i= 3/10{
  2. rdplot trust_cadre_dummy agediff if  age<=60+`i'&age>=60-`i'&rural==1,   c(0) p(1) binselect(qspr)       ///
>                     graph_options(title(Satisfaction with local government,size(medsmall))  xline(0) xlabel(-`i'[2]`i') ylabel(0[0.2]1)  ///
>                      ytitle("Trust in Local Officials Dummy",size(small)) legend(off) ///
>                      xtitle(Age relative to 60,height(5)) ylab(,nogrid)  ///
>                      title("Bandwidth `i'", size(small))  graphregion(color(white)))
  3.                                          graph save Graph "rd`i'.gph", replace
  4. 
. } 

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       1306
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |       564         742
   Eff. Number of obs |       564         742
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     3.000       3.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        11          11
   Average bin length |     1.000       0.750
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        11          11
  Mimicking Var. bins |        26          26
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd3.gph not found)
file rd3.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       1615
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |       738         877
   Eff. Number of obs |       738         877
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     4.000       4.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        15          16
   Average bin length |     1.000       0.800
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        15          16
  Mimicking Var. bins |        30          30
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd4.gph not found)
file rd4.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       1897
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |       851        1046
   Eff. Number of obs |       851        1046
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     5.000       5.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        16          18
   Average bin length |     1.000       0.833
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        16          18
  Mimicking Var. bins |        34          34
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd5.gph not found)
file rd5.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       2168
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1002        1166
   Eff. Number of obs |      1002        1166
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     6.000       6.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        18          18
   Average bin length |     1.000       0.857
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        18          18
  Mimicking Var. bins |        37          37
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd6.gph not found)
file rd6.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       2405
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1117        1288
   Eff. Number of obs |      1117        1288
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     7.000       7.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        15          20
   Average bin length |     1.000       0.875
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        15          20
  Mimicking Var. bins |        40          40
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd7.gph not found)
file rd7.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       2763
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1374        1389
   Eff. Number of obs |      1374        1389
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     8.000       8.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        16          21
   Average bin length |     1.000       0.889
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        16          21
  Mimicking Var. bins |        45          45
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd8.gph not found)
file rd8.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       3103
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1623        1480
   Eff. Number of obs |      1623        1480
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |     9.000       9.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        17          18
   Average bin length |     1.000       0.900
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        17          18
  Mimicking Var. bins |        49          49
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd9.gph not found)
file rd9.gph saved

RD Plot with quantile spaced number of bins using polynomial regression.

         Cutoff c = 0 | Left of c  Right of c        Number of obs  =       3426
----------------------+----------------------        Kernel         =    Uniform
        Number of obs |      1848        1578
   Eff. Number of obs |      1848        1578
  Order poly. fit (p) |         1           1
     BW poly. fit (h) |    10.000      10.000
 Number of bins scale |     1.000       1.000

Outcome: trust_cadre_dummy. Running variable: agediff.
---------------------------------------------
                      | Left of c  Right of c
----------------------+----------------------
        Bins selected |        11          24
   Average bin length |     1.111       0.909
    Median bin length |     1.000       1.000
----------------------+----------------------
    IMSE-optimal bins |        11          24
  Mimicking Var. bins |        52          52
----------------------+----------------------
Rel. to IMSE-optimal: | 
        Implied scale |     1.000       1.000
    WIMSE var. weight |     0.500       0.500
    WIMSE bias weight |     0.500       0.500
---------------------------------------------

(file rd10.gph not found)
file rd10.gph saved

. gr combine rd3.gph rd4.gph rd5.gph rd6.gph rd7.gph rd8.gph rd9.gph rd10.gph , subtitle(, color(black) fcolor(white) lcolor(white)) graphregion(fcolor(white) lcolor(white) ifcolor(white)
>  ilcolor(white))       graphregion(fcolor(white)) row(2)       

. 
. 
.                                         
. graph export "$path\fig_a11.pdf", as(pdf) replace
file F:\Dropbox\Research\PSRM\replication file\fig_a11.pdf saved as PDF format

. 
.  
.  
.  
. forval i= 3/10{
  2. erase rd`i'.gph
  3. }

. 
. 
. 
. 
. 
.  *************************************************************
. ****Table A-18 
. **************************************************************
. 
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.   
. ***CFPS, raw
.  use "CFPS_2014.dta",clear

. 
.  ivreg2   trust_cadre_dummy (NRPP=agedummy agediff)  agediff male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural  if age<=63&age>=57 &rural==1
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     1278
                                                      F(  9,  1268) =     0.03
                                                      Prob > F      =   1.0000
Total (centered) SS     =  318.7957746                Centered R2   = -47.9900
Total (uncentered) SS   =          609                Uncentered R2 = -24.6450
Residual SS             =  15617.79474                Root MSE      =    3.496

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -9.379135    59.8766    -0.16   0.876    -126.7351    107.9768
         agediff |   1.023292   6.469208     0.16   0.874    -11.65612    13.70271
            male |   -.082818   .4918288    -0.17   0.866    -1.046785    .8811488
       school_yr |  -.0217645   .1384425    -0.16   0.875    -.2931068    .2495778
           hukou |   1.703158   11.21356     0.15   0.879    -20.27502    23.68134
             ccp |  -.2025659   1.850893    -0.11   0.913    -3.830249    3.425117
   log_pop_10_13 |   .1679745   .9191497     0.18   0.855    -1.633526    1.969475
log_gdp_pc_10_13 |  -.3436024   2.199559    -0.16   0.876    -4.654658    3.967453
       pro_rural |   .9438565   4.406701     0.21   0.830    -7.693119    9.580832
           _cons |    3.20646   19.55246     0.16   0.870    -35.11566    41.52858
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):           0.025
                                                   Chi-sq(1) P-val =    0.8734
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                0.025
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------

. outreg2 using "Table_A18.doc", keep(NRPP) se  bdec(3) sdec(3) nocons replace
Table_A18.doc
dir : seeout

. 
. 
. forval i= 4/10{
  2. ivreg2   trust_cadre_dummy (NRPP=agedummy agediff)  agediff male school_yr hukou ccp  log_pop_10_13  log_gdp_pc_10_13 pro_rural  if age<=60+`i'&age>=60-`i' &rural==1
  3. outreg2 using "Table_A18.doc", keep(NRPP) se  bdec(3) sdec(3) nocons append
  4. } 
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     1576
                                                      F(  9,  1566) =     1.54
                                                      Prob > F      =   0.1274
Total (centered) SS     =  393.2227157                Centered R2   =  -0.0095
Total (uncentered) SS   =          753                Uncentered R2 =   0.4728
Residual SS             =  396.9615419                Root MSE      =    .5019

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.1241598   .5104109    -0.24   0.808    -1.124547    .8762272
         agediff |   .0124463   .0435857     0.29   0.775    -.0729802    .0978728
            male |  -.0238747   .0287087    -0.83   0.406    -.0801426    .0323933
       school_yr |  -.0005475   .0037194    -0.15   0.883    -.0078373    .0067423
           hukou |  -.0080462   .1391565    -0.06   0.954     -.280788    .2646957
             ccp |   .1019541   .0544469     1.87   0.061      -.00476    .2086681
   log_pop_10_13 |   .0211313   .0321233     0.66   0.511    -.0418292    .0840917
log_gdp_pc_10_13 |  -.0157489   .0477213    -0.33   0.741     -.109281    .0777831
       pro_rural |   .2304033   .1591663     1.45   0.148    -.0815569    .5423636
           _cons |    .423919   .6037533     0.70   0.483    -.7594157    1.607254
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):           7.070
                                                   Chi-sq(1) P-val =    0.0078
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                7.056
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     1847
                                                      F(  9,  1837) =     1.97
                                                      Prob > F      =   0.0391
Total (centered) SS     =  460.4233893                Centered R2   =   0.0091
Total (uncentered) SS   =          874                Uncentered R2 =   0.4780
Residual SS             =  456.2295536                Root MSE      =     .497

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.0065567   .2961706    -0.02   0.982    -.5870404     .573927
         agediff |   .0015053   .0216177     0.07   0.944    -.0408646    .0438752
            male |  -.0068838   .0259529    -0.27   0.791    -.0577506     .043983
       school_yr |  -.0007838    .003143    -0.25   0.803    -.0069441    .0053764
           hukou |  -.1122028   .1171086    -0.96   0.338    -.3417314    .1173259
             ccp |   .0872309   .0467677     1.87   0.062     -.004432    .1788939
   log_pop_10_13 |   .0025865   .0298577     0.09   0.931    -.0559335    .0611065
log_gdp_pc_10_13 |  -.0317366   .0408097    -0.78   0.437    -.1117221    .0482488
       pro_rural |   .1764916   .1450325     1.22   0.224    -.1077669      .46075
           _cons |   .7952138   .5158792     1.54   0.123    -.2158908    1.806318
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):          19.892
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               20.000
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     2108
                                                      F(  9,  2098) =     2.58
                                                      Prob > F      =   0.0059
Total (centered) SS     =  525.2348197                Centered R2   =   0.0108
Total (uncentered) SS   =          993                Uncentered R2 =   0.4768
Residual SS             =  519.5796047                Root MSE      =    .4965

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.0031752   .2232627    -0.01   0.989    -.4407619    .4344116
         agediff |   .0004818   .0142659     0.03   0.973    -.0274789    .0284425
            male |  -.0029172   .0241006    -0.12   0.904    -.0501536    .0443191
       school_yr |   -.002778   .0029683    -0.94   0.349    -.0085957    .0030398
           hukou |  -.0975289   .1099296    -0.89   0.375    -.3129871    .1179292
             ccp |   .0837907   .0435409     1.92   0.054    -.0015479    .1691292
   log_pop_10_13 |   .0093821   .0268485     0.35   0.727    -.0432399    .0620041
log_gdp_pc_10_13 |  -.0357566   .0371968    -0.96   0.336    -.1086609    .0371477
       pro_rural |   .1955941   .1341197     1.46   0.145    -.0672756    .4584638
           _cons |   .7721894   .4787291     1.61   0.107    -.1661025    1.710481
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):          35.429
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               35.864
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     2339
                                                      F(  9,  2329) =     2.79
                                                      Prob > F      =   0.0030
Total (centered) SS     =  582.6849081                Centered R2   =   0.0051
Total (uncentered) SS   =         1100                Uncentered R2 =   0.4730
Residual SS             =  579.7338365                Root MSE      =    .4979

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.0663855   .1783092    -0.37   0.710    -.4158651    .2830942
         agediff |    .005437   .0102057     0.53   0.594    -.0145659    .0254398
            male |  -.0059996   .0230938    -0.26   0.795    -.0512626    .0392634
       school_yr |   -.003948   .0027368    -1.44   0.149    -.0093121    .0014162
           hukou |  -.0764302   .1011221    -0.76   0.450    -.2746259    .1217654
             ccp |    .057124   .0413713     1.38   0.167    -.0239622    .1382103
   log_pop_10_13 |   .0161221   .0253525     0.64   0.525    -.0335678     .065812
log_gdp_pc_10_13 |  -.0380548    .035564    -1.07   0.285    -.1077589    .0316492
       pro_rural |   .1885178    .127322     1.48   0.139    -.0610287    .4380643
           _cons |   .7613193   .4571471     1.67   0.096    -.1346726    1.657311
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):          55.241
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               56.335
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     2685
                                                      F(  9,  2675) =     3.46
                                                      Prob > F      =   0.0003
Total (centered) SS     =   668.590689                Centered R2   =  -0.0042
Total (uncentered) SS   =         1258                Uncentered R2 =   0.4663
Residual SS             =  671.3731858                Root MSE      =       .5

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.1370831   .1384774    -0.99   0.322    -.4084937    .1343276
         agediff |   .0114049   .0067468     1.69   0.091    -.0018187    .0246284
            male |  -.0143687   .0213435    -0.67   0.501    -.0562012    .0274638
       school_yr |  -.0027667    .002545    -1.09   0.277    -.0077547    .0022214
           hukou |   -.058692   .0968705    -0.61   0.545    -.2485546    .1311706
             ccp |   .0699331   .0388388     1.80   0.072    -.0061894    .1460557
   log_pop_10_13 |   .0152219   .0234886     0.65   0.517    -.0308149    .0612586
log_gdp_pc_10_13 |  -.0367147   .0328152    -1.12   0.263    -.1010314    .0276019
       pro_rural |   .1778299   .1194537     1.49   0.137     -.056295    .4119549
           _cons |   .7601237    .422009     1.80   0.072    -.0669988    1.587246
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):          95.656
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               98.820
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     3012
                                                      F(  9,  3002) =     3.68
                                                      Prob > F      =   0.0001
Total (centered) SS     =   749.876162                Centered R2   =   0.0031
Total (uncentered) SS   =         1409                Uncentered R2 =   0.4694
Residual SS             =  747.5805023                Root MSE      =    .4982

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.0968954   .1144363    -0.85   0.397    -.3211863    .1273956
         agediff |   .0087026   .0048856     1.78   0.075     -.000873    .0182783
            male |  -.0147715   .0200019    -0.74   0.460    -.0539744    .0244315
       school_yr |  -.0020802   .0023833    -0.87   0.383    -.0067514    .0025911
           hukou |  -.0718201   .0919441    -0.78   0.435    -.2520273    .1083871
             ccp |   .0630466   .0367312     1.72   0.086    -.0089453    .1350385
   log_pop_10_13 |   .0124222   .0218985     0.57   0.571    -.0304981    .0553424
log_gdp_pc_10_13 |  -.0286777   .0309465    -0.93   0.354    -.0893316    .0319762
       pro_rural |   .1861707   .1130148     1.65   0.099    -.0353342    .4076757
           _cons |    .691557    .399527     1.73   0.083    -.0915016    1.474616
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):         143.829
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              150.540
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout
Warning - duplicate variables detected
Duplicates:         agediff

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

                                                      Number of obs =     3324
                                                      F(  9,  3314) =     3.76
                                                      Prob > F      =   0.0001
Total (centered) SS     =   826.448556                Centered R2   =   0.0064
Total (uncentered) SS   =         1539                Uncentered R2 =   0.4664
Residual SS             =  821.1574611                Root MSE      =     .497

----------------------------------------------------------------------------------
trust_cadre_du~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            NRPP |  -.0601751   .1022101    -0.59   0.556    -.2605033    .1401531
         agediff |   .0063102   .0039359     1.60   0.109     -.001404    .0140243
            male |  -.0127408   .0188545    -0.68   0.499    -.0496949    .0242133
       school_yr |  -.0023091    .002258    -1.02   0.306    -.0067347    .0021165
           hukou |  -.1023599   .0844059    -1.21   0.225    -.2677924    .0630726
             ccp |   .0777349   .0351917     2.21   0.027     .0087604    .1467093
   log_pop_10_13 |   .0061592   .0208935     0.29   0.768    -.0347912    .0471096
log_gdp_pc_10_13 |  -.0091335   .0291676    -0.31   0.754    -.0663008    .0480339
       pro_rural |   .2211743   .1081363     2.05   0.041     .0092311    .4331175
           _cons |   .5249008   .3767711     1.39   0.164    -.2135569    1.263359
----------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic):         183.675
                                                   Chi-sq(1) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              193.833
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments):           0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         NRPP
Included instruments: agediff male school_yr hukou ccp log_pop_10_13
                      log_gdp_pc_10_13 pro_rural
Excluded instruments: agedummy
Duplicates:           agediff
------------------------------------------------------------------------------
Table_A18.doc
dir : seeout

. 
. 
. 
. 
. 
. 
.  *************************************************************
. ****Table A-19
. **************************************************************
. 
. 
. ** ssc install psmatch2
. 
. 
.   cd "$path\data"
F:\Dropbox\Research\PSRM\replication file\data

.   
.   
. ***CFPS, raw
.  use "CFPS_2014.dta",clear

. 
. 
. 
. eststo clear

. 
. local calipers "0.001 0.01 0.05"

. 
. local j = 1

. foreach c of local calipers {
  2. 
.   quietly psmatch2 NRPP logfincome age male school_yr hukou ccp log_pop_10_13 log_gdp_pc_10_13 pro_rural if rural == 1, out(trust_cadre_dummy) neighbor(3) caliper(`c')
  3.                 
.     scalar att = r(att)
  4.     scalar se  = r(seatt)
  5. 
.     matrix b = (att)
  6.     matrix V = (se^2)
  7. 
.     matrix colnames b = NRPS
  8.     matrix rownames V = NRPS
  9.     matrix colnames V = NRPS
 10. 
.         scalar Ntotal = 8152   
 11.         
.     ereturn post b V, obs(`=Ntotal')
 12.     eststo m`j'
 13. 
.     local ++j
 14. }

. 
. 
. 
. 
. esttab m1 m2 m3 using "Table_A19.doc", replace b(3) se(3) star(* 0.10 ** 0.05 *** 0.01) ///
>     mtitles("Caliper=0.001" "Caliper=0.01" "Caliper=0.05") ///
>     coeflabels(NRPS "New Rural Pension Scheme (NRPS)") ///
>     stats(N, labels("N") fmt(0)) nonotes
(file Table_A19.doc not found)
(output written to Table_A19.doc)

.  
.  
.  
.  
.  
.  
.  
. log close
      name:  <unnamed>
       log:  F:\Dropbox\Research\PSRM\replication file\data\appendix.log
  log type:  text
 closed on:  10 Dec 2025, 16:40:48
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
